Crop constituent map generation and control system

ABSTRACT

One or more maps are obtained by an agricultural work machine. The one or more maps map one or more agricultural characteristic values at different geographic locations of a field. An in-situ sensor on the agricultural work machine senses an agricultural characteristic as the agricultural work machine moves through the field. A predictive map generator generates a predictive map that predicts a predictive agricultural characteristic at different locations in the field based on a relationship between the values in the one or more maps and the agricultural characteristic sensed by the in-situ sensor. The predictive map can be output and used in automated machine control.

FIELD OF THE DESCRIPTION

The present description relates to agricultural machines, forestry machines, construction machines and turf management machines.

BACKGROUND

There are a wide variety of different types of agricultural machines. Some agricultural machines include harvesters, such as combine harvesters, sugar cane harvesters, cotton harvesters, self-propelled forage harvesters, and windrowers. Some harvesters can also be fitted with different types of heads to harvest different types of crops.

A variety of different conditions in fields have a number of deleterious effects on the harvesting operation. Therefore, an operator may attempt to modify control of the harvester, upon encountering such conditions during a harvesting operation.

The discussion above is merely provided for general background information and is not intended to be used as an aid in determining the scope of the claimed subject matter.

SUMMARY

One or more maps are obtained by an agricultural work machine. The one or more maps map one or more agricultural characteristic values at different geographic locations of a field. An in-situ sensor on the agricultural work machine senses an agricultural characteristic as the agricultural work machine moves through the field. A predictive map generator generates a predictive map that predicts a predictive agricultural characteristic at different locations in the field based on a relationship between the values in the one or more maps and the agricultural characteristic sensed by the in-situ sensor. The predictive map can be output and used in automated machine control.

This Summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter. The claimed subject matter is not limited to examples that solve any or all disadvantages noted in the background.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a partial pictorial, partial schematic illustration of one example of an agricultural harvester.

FIG. 2 is a block diagram showing some portions of an agricultural harvester in more detail, according to some examples of the present disclosure.

FIGS. 3A-3B (collectively referred to herein as FIG. 3) show a flow diagram illustrating an example of operation of an agricultural harvester in generating a map.

FIG. 4 is a block diagram showing one example of a predictive model generator and a predictive map generator.

FIG. 5 is a flow diagram showing an example of operation of an agricultural harvester in receiving a map, detecting an in-situ characteristic, and generating a functional predictive map for presentation or use in controlling the agricultural harvester during a harvesting operation or both.

FIG. 6A is a block diagram showing one example of a predictive model generator and a predictive map generator.

FIG. 6B is a block diagram showing some examples of in-situ sensors.

FIG. 7 shows a flow diagram illustrating one example of operation of an agricultural harvester involving generating a functional predictive map using a prior information map and an in-situ sensor input.

FIG. 8 is a block diagram showing one example of a control zone generator.

FIG. 9 is a flow diagram illustrating one example of the operation of the control zone generator shown in FIG. 8.

FIG. 10 illustrates a flow diagram showing an example of operation of a control system in selecting a target settings value to control an agricultural harvester.

FIG. 11 is a block diagram showing one example of an operator interface controller.

FIG. 12 is a flow diagram illustrating one example of an operator interface controller.

FIG. 13 is a pictorial illustration showing one example of an operator interface display.

FIG. 14 is a block diagram showing one example of an agricultural harvester in communication with a remote server environment.

FIGS. 15-17 show examples of mobile devices that can be used in an agricultural harvester.

FIG. 18 is a block diagram showing one example of a computing environment that can be used in an agricultural harvester.

DETAILED DESCRIPTION

For the purposes of promoting an understanding of the principles of the present disclosure, reference will now be made to the examples illustrated in the drawings, and specific language will be used to describe the same. It will nevertheless be understood that no limitation of the scope of the disclosure is intended. Any alterations and further modifications to the described devices, systems, methods, and any further application of the principles of the present disclosure are fully contemplated as would normally occur to one skilled in the art to which the disclosure relates. In particular, it is fully contemplated that the features, components, steps, or a combination thereof described with respect to one example may be combined with the features, components, steps, or a combination thereof described with respect to other examples of the present disclosure.

The present description relates to using in-situ data taken concurrently with an agricultural operation, in combination with prior data, to generate a functional predictive map and, more particularly, a functional predictive crop constituents map. In some examples, the functional predictive crop constituents map can be used to control an agricultural work machine, such as an agricultural harvester. It may be desirable to change or otherwise control the machine settings of the agricultural harvester depending on the crop constituent values in the area the agricultural harvester is operating in. In some situations, high crop constituent values such as protein or oil may result in premium market prices or in benefits when fed to livestock. To capture this value, crop is segregated at harvest and managed based on the constituent level. Segregation may be done by directing crop to one of a plurality of on-board clean grain tanks. In other examples, the segregation may be done by off-loading grain to a grain transport vehicle as constituent level thresholds are crossed. In still other examples, the segregation may be done by managing the path a harvesting vehicle takes through the field based on predicted constituent values.

In one example, the systems herein can obtain an agricultural characteristic map that maps values of one or more agricultural characteristics to different locations in one or more field(s) of interest. Thus, the agricultural characteristic maps provide georeferenced agricultural characteristic values that indicate values of one or more agricultural characteristics at different locations in a field of interest. Agricultural characteristics can include any characteristic that may affect an agricultural operation. For example, but not by limitation, agricultural characteristics can include crop or vegetation (such as weeds) characteristics, soil properties, topographic characteristics of the field, machine characteristics of the agricultural work machine (such as machine settings, operating characteristics, or machine performance characteristics) for instance, machine speed or power utilization, yield, biomass, seeding characteristics (seed genotype, seed population, seed spacing, as well as various other seeding characteristics), crop state, compaction, agricultural characteristics detected during a prior operation, as well as any of a number of agricultural characteristics. Some examples of particular agricultural characteristics maps will now be described.

A vegetative index map illustratively maps vegetative index values, which may be indicative of vegetative growth, across different geographic locations in one or more field(s) of interest. One example of a vegetative index includes a normalized difference vegetation index (NDVI). There are many other vegetative indices, and all of these vegetative indices are within the scope of the present disclosure. In some examples, a vegetative index may be derived from sensor readings of one or more bands of electromagnetic radiation reflected by the plants. Without limitations, these bands may be in the microwave, infrared, visible, or ultraviolet portions of the electromagnetic spectrum.

A vegetative index map can thus be used to identify the presence and location of vegetation. In some examples, a vegetative index map enables crops to be identified and georeferenced in the presence of bare soil, crop residue, or other plants, including crop or weeds. For instance, towards the beginning of a growing season, when a crop is in a growing state, the vegetative index may show the progress of the crop development. Therefore, if a vegetative index map is generated early in the growing season or midway through the growing season, the vegetative index map may be indicative of the progress of the development of the crop plants. For instance, the vegetative index map may indicate whether the plant is stunted, establishing a sufficient canopy, or other plant attributes that are indicative of plant development.

A prior operation map maps values of various prior operation characteristics to different locations in one or more field(s) of interest based on data collected during an operation prior to the agricultural harvesting operation. Prior operations by different machine may have an effect on the operation of an agricultural harvester. For instance, a prior spraying or substance application operation may have an effect or otherwise have some relationship with resulting constituents in crops on a field to be harvested. For example, the application of fertilizer, such as fertilizer with nitrogen, the application of herbicide, pesticide, fungicide, as well as various other substances, may have an effect on or share a relationship with resulting crop constituents, including constituents of grains of crops. For instance, the application of fertilizer, such as fertilizer with nitrogen, may have an effect or otherwise have a relationship with the resulting constituents of crops on a field, including constituents of grains in crops. Thus, a prior operation map provides georeferenced prior operation characteristics, as agricultural characteristics, that are based on data collected during a prior operation. In one example, a prior operation map may include georeferenced substance (such as fertilizer) application values that indicate substance application characteristics, such as locations, amounts, types, and compositions of substance applied by the agricultural work machine, such as a sprayer, seeder, spreader, as well as various other substance application machines.

A historical crop constituent map illustratively maps crop constituent values across different geographic locations in one or more field(s) of interest. These historical crop constituent maps are collected from past agricultural operations on the field(s), such as past harvesting operations. A crop constituent map may show crop constituents in crop constituent value units. One example of a crop constituent value unit includes a numeric value, such as a percentage, weight value, or mass value, that indicates an amount of a constituent in a crop, for example an amount of protein, starch, oil, nutrients, water, among various other constituents of crop or vegetation, or an amount of protein, starch, oil, nutrients, water, among various other constituents, in grain of crop plants. Some crop constituents are more transient in nature, in that the amount of the constituent contained within the crop material (such as grain) will vary over time, for instance, grain can dry or take on water throughout a span of time. Some crop constituents are more structural in nature, in that the amount of constituent (or ratios of constituents) tend to not vary as much over time, at least until the grain decomposes. As used herein, crop constituents can also refer to grain constituents, and thus, crop constituent values can, in some example, refer to an amount of constituent in the grain of crop plants, for example, the amount of protein, starch, oil, nutrients, water, among various other constituents, in the grain of crop plants. In some examples, a historical crop constituent map may be derived from sensor readings of one or more crop constituent sensors. Without limitation, these crop constituent sensors may utilize one or more bands of electromagnetic radiation in detecting crop constituents. For example, a crop constituent sensor may utilize the reflectance or absorption of various ranges (e.g., various wavelengths or frequencies, or both) of electromagnetic radiation by crop or other vegetation material in detecting crop constituents. In some examples, a crop constituent sensor may include an optical sensor, such as an optical spectrometer. In one example, a crop constituent sensor may utilize near-infrared spectroscopy or visible and near-infrared spectroscopy.

A soil property map illustratively maps soil property values (which may be indicative of soil type, soil moisture, soil cover, soil structure, soil composition, as well as various other soil properties) across different geographic locations in one or more field(s) of interest. The soil property maps thus provide geo-referenced soil properties across a field of interest. Soil type can refer to taxonomic units in soil science, wherein each soil type includes defined sets of shared properties. Soil types can include, for example, sandy soil, clay soil, silt soil, peat soil, chalk soil, loam soil, and various other soil types. Soil moisture can refer to the amount of water that is held or otherwise contained in the soil. Soil moisture can also be referred to as soil wetness. Soil cover can refer to the amount of items or materials covering the soil, including, vegetation material, such as crop residue or cover crop, debris, as well as various other items or materials. Commonly, in agricultural terms, soil cover includes a measure of remaining crop residue, such as a remaining mass of plant stalks, as well as a measure of cover crop. Soil structure can refer to the arrangement of solid parts of the soil and the pore space located between the solid parts of the soil. Soil structure can include the way in which individual particles, such as individual particles of sand, silt, and clay, are assembled. Soil structure can be described in terms of grade (degree of aggregation), class (average size of aggregates), and form (types of aggregates), as well as a variety of other descriptions. Soil composition can include a number of soil composition characteristics, such as locations, amounts, and types of constituents in soil, such as nitrogen, and other plant nutrients. In one example, a soil composition may include values indicative of an amount, location, and type of soil constituent in a field of interest, for instance, a soil composition map may be a nitrogen map that indicates an amount of nitrogen present in soil in different locations in a field of interest. These are merely examples. Various other characteristics and properties of the soil can be mapped as soil property values on a soil property map

In some examples, the system can receive a predictive characteristic map. A predictive characteristic map illustratively maps predictive characteristic values across one or more geographic locations in one or more field(s) of interest. For instance, a predictive characteristic map can be a predictive biomass map that maps predictive values of biomass or biomass characteristics (e.g., vegetation height, vegetation density, vegetation mass, vegetation volume, threshing rotor drive force) across one or more geographic locations in one or more field(s) of interest. In another example, a predictive characteristic map can be a predictive yield map that maps predictive yield values across one or more geographic locations in one or more field(s) of interest. In another example, a predictive characteristic map can be a predictive crop moisture map that maps predictive values of crop moisture across one or more geographic locations in one or more field(s) of interest.

The present discussion thus proceeds with respect to examples in which a system receives one or more agricultural characteristic maps, for example, but not by limitation, one or more of a vegetative index map, one or more predictive maps (such as a predictive yield map, a predictive moisture map, or a predictive biomass map), a prior operation map (such as a prior operation map showing substance application characteristics), a historical crop constituent map of a field, or a soil property map (such as a nitrogen map) and also uses an in-situ sensor to detect a characteristic or variable indicative of crop constituent characteristics during a harvesting operation. These are merely examples of some of the maps that may be received. Various other agricultural characteristic maps are also contemplated herein. The system generates a model that models a relationship between the agricultural characteristic values, the vegetative index values, the predictive characteristic values (such as predictive biomass values, predictive yield values, or predictive crop moisture values), the prior operation characteristic values (such as substance application characteristic values), the historical crop constituent values, or the soil property values, such as soil composition values (such as nitrogen values), from one or more of the received maps and the in-situ data from the in-situ sensor. The model is used to generate a functional predictive crop constituent map that predicts crop constituent values in the field. The functional predictive crop constituent map, generated during the harvesting operation, can be presented to an operator or other user or used in automatically controlling an agricultural harvester during the harvesting operation or both.

FIG. 1 is a partial pictorial, partial schematic, illustration of a self-propelled agricultural harvester 100. In the illustrated example, agricultural harvester 100 is a combine harvester. Further, although combine harvesters are provided as examples throughout the present disclosure, it will be appreciated that the present description is also applicable to other types of harvesters, such as cotton harvesters, sugarcane harvesters, self-propelled forage harvesters, windrowers, or other agricultural work machines. Consequently, the present disclosure is intended to encompass the various types of harvesters described and is, thus, not limited to combine harvesters. Moreover, the present disclosure is directed to other types of work machines, such as agricultural seeders and sprayers, construction equipment, forestry equipment, and turf management equipment where generation of a predictive map may be applicable. Consequently, the present disclosure is intended to encompass these various types of harvesters and other work machines and is, thus, not limited to combine harvesters.

As shown in FIG. 1, agricultural harvester 100 illustratively includes an operator compartment 101, which can have a variety of different operator interface mechanisms, for controlling agricultural harvester 100. Agricultural harvester 100 includes front-end equipment, such as a header 102, and a cutter generally indicated at 104. Agricultural harvester 100 also includes a feeder house 106, a feed accelerator 108, and a thresher generally indicated at 110. The feeder house 106 and the feed accelerator 108 form part of a material handling subsystem 125. Header 102 is pivotally coupled to a frame 103 of agricultural harvester 100 along pivot axis 105. One or more actuators 107 drive movement of header 102 about axis 105 in the direction generally indicated by arrow 109. Thus, a vertical position of header 102 (the header height) above ground 111 over which the header 102 travels is controllable by actuating actuator 107. While not shown in FIG. 1, agricultural harvester 100 may also include one or more actuators that operate to apply a tilt angle, a roll angle, or both to the header 102 or portions of header 102. Tilt refers to an angle at which the cutter 104 engages the crop. The tilt angle is increased, for example, by controlling header 102 to point a distal edge 113 of cutter 104 more toward the ground. The tilt angle is decreased by controlling header 102 to point the distal edge 113 of cutter 104 more away from the ground. The roll angle refers to the orientation of header 102 about the front-to-back longitudinal axis of agricultural harvester 100.

Thresher 110 illustratively includes a threshing rotor 112 and a set of concaves 114. Further, agricultural harvester 100 also includes a separator 116. Agricultural harvester 100 also includes a cleaning subsystem or cleaning shoe (collectively referred to as cleaning subsystem 118) that includes a cleaning fan 120, chaffer 122, and sieve 124. The material handling subsystem 125 also includes discharge beater 126, tailings elevator 128, clean grain elevator 130, as well as unloading auger 134 and spout 136. The clean grain elevator moves clean grain into clean grain tank 132. Agricultural harvester 100 also includes a residue subsystem 138 that can include chopper 140 and spreader 142. Agricultural harvester 100 also includes a propulsion subsystem that includes an engine that drives ground engaging components 144, such as wheels or tracks. In some examples, a combine harvester within the scope of the present disclosure may have more than one of any of the subsystems mentioned above. In some examples, agricultural harvester 100 may have left and right cleaning subsystems, separators, etc., which are not shown in FIG. 1.

In operation, and by way of overview, agricultural harvester 100 illustratively moves through a field in the direction indicated by arrow 147. As agricultural harvester 100 moves, header 102 (and the associated reel 164) engages the crop to be harvested and gathers the crop toward cutter 104. An operator of agricultural harvester 100 can be a local human operator, a remote human operator, or an automated system. An operator command is a command by an operator. The operator of agricultural harvester 100 may determine one or more of a height setting, a tilt angle setting, or a roll angle setting for header 102. For example, the operator inputs a setting or settings to a control system, described in more detail below, that controls actuator 107. The control system may also receive a setting from the operator for establishing the tilt angle and roll angle of the header 102 and implement the inputted settings by controlling associated actuators, not shown, that operate to change the tilt angle and roll angle of the header 102. The actuator 107 maintains header 102 at a height above ground 111 based on a height setting and, where applicable, at desired tilt and roll angles. Each of the height, roll, and tilt settings may be implemented independently of the others. The control system responds to header error (e.g., the difference between the height setting and measured height of header 104 above ground 111 and, in some examples, tilt angle and roll angle errors) with a responsiveness that is determined based on a selected sensitivity level. If the sensitivity level is set at a greater level of sensitivity, the control system responds to smaller header position errors, and attempts to reduce the detected errors more quickly than when the sensitivity is at a lower level of sensitivity.

Returning to the description of the operation of agricultural harvester 100, after crops are cut by cutter 104, the severed crop material is moved through a conveyor in feeder house 106 toward feed accelerator 108, which accelerates the crop material into thresher 110. The crop material is threshed by rotor 112 rotating the crop against concaves 114. The threshed crop material is moved by a separator rotor in separator 116 where a portion of the residue is moved by discharge beater 126 toward the residue subsystem 138. The portion of residue transferred to the residue subsystem 138 is chopped by residue chopper 140 and spread on the field by spreader 142. In other configurations, the residue is released from the agricultural harvester 100 in a windrow. In other examples, the residue subsystem 138 can include weed seed eliminators (not shown) such as seed baggers or other seed collectors, or seed crushers or other seed destroyers.

Grain falls to cleaning subsystem 118. Chaffer 122 separates some larger pieces of material from the grain, and sieve 124 separates some of finer pieces of material from the clean grain. Clean grain falls to an auger that moves the grain to an inlet end of clean grain elevator 130, and the clean grain elevator 130 moves the clean grain upwards, depositing the clean grain in clean grain tank 132. Residue is removed from the cleaning subsystem 118 by airflow generated by cleaning fan 120. Cleaning fan 120 directs air along an airflow path upwardly through the sieves and chaffers. The airflow carries residue rearwardly in agricultural harvester 100 toward the residue handling subsystem 138.

Tailings elevator 128 returns tailings to thresher 110 where the tailings are re-threshed. Alternatively, the tailings also may be passed to a separate re-threshing mechanism by a tailings elevator or another transport device where the tailings are re-threshed as well.

FIG. 1 also shows that, in one example, agricultural harvester 100 includes ground speed sensor 146, one or more separator loss sensors 148, a clean grain camera 150, a forward looking image capture mechanism 151, which may be in the form of a stereo or mono camera, and one or more loss sensors 152 provided in the cleaning subsystem 118.

Ground speed sensor 146 senses the travel speed of agricultural harvester 100 over the ground. Ground speed sensor 146 may sense the travel speed of the agricultural harvester 100 by sensing the speed of rotation of the ground engaging components (such as wheels or tracks), a drive shaft, an axel, or other components. In some instances, the travel speed may be sensed using a positioning system, such as a global positioning system (GPS), a dead reckoning system, a long range navigation (LORAN) system, or a wide variety of other systems or sensors that provide an indication of travel speed.

Loss sensors 152 illustratively provide an output signal indicative of the quantity of grain loss occurring in both the right and left sides of the cleaning subsystem 118. In some examples, sensors 152 are strike sensors which count grain strikes per unit of time or per unit of distance traveled to provide an indication of the grain loss occurring at the cleaning subsystem 118. The strike sensors for the right and left sides of the cleaning subsystem 118 may provide individual signals or a combined or aggregated signal. In some examples, sensors 152 may include a single sensor as opposed to separate sensors provided for each cleaning subsystem 118. Separator loss sensor 148 provides a signal indicative of grain loss in the left and right separators, not separately shown in FIG. 1. The separator loss sensors 148 may be associated with the left and right separators and may provide separate grain loss signals or a combined or aggregate signal. In some instances, sensing grain loss in the separators may also be performed using a wide variety of different types of sensors as well.

Agricultural harvester 100 may also include other sensors and measurement mechanisms. For instance, agricultural harvester 100 may include one or more of the following sensors: a header height sensor that senses a height of header 102 above ground 111; stability sensors that sense oscillation or bouncing motion (and amplitude) of agricultural harvester 100; a residue setting sensor that is configured to sense whether agricultural harvester 100 is configured to chop the residue, produce a windrow, etc.; a cleaning shoe fan speed sensor to sense the speed of cleaning fan 120; a concave clearance sensor that senses clearance between the rotor 112 and concaves 114; a threshing rotor speed sensor that senses a rotor speed of rotor 112; a chaffer clearance sensor that senses the size of openings in chaffer 122; a sieve clearance sensor that senses the size of openings in sieve 124; a material other than grain (MOG) moisture sensor, such as a capacitive moisture sensor, that senses a moisture level of the MOG passing through agricultural harvester 100; one or more machine setting sensors configured to sense various configurable settings of agricultural harvester 100; a machine orientation sensor that senses the orientation of agricultural harvester 100; and crop property sensors that sense a variety of different types of crop properties, such as crop type, crop moisture, crop constituent characteristics, and other crop properties. Crop property sensors may also be configured to sense characteristics of the severed crop material as the crop material is being processed by agricultural harvester 100. For example, in some instances, the crop property sensors may sense grain quality such as broken grain, MOG levels; grain constituents such as starches and protein; and grain feed rate as the grain travels through the feeder house 106, clean grain elevator 130, or elsewhere in the agricultural harvester 100. The crop property sensors may also sense the feed rate of biomass through feeder house 106, through the separator 116 or elsewhere in agricultural harvester 100. The crop property sensors may also sense the feed rate as a mass flow rate of grain through elevator 130 or through other portions of the agricultural harvester 100 or provide other output signals indicative of other sensed variables. Crop property sensors can include one or more crop constituent sensors that sense characteristics indicative of crop constituents of crops, including, characteristics of constituents of grains of crops.

Without limitation, crop constituent sensors may utilize one or more bands of electromagnetic radiation in detecting crop constituents. For example, a crop constituent sensor may utilize the reflectance or absorption of various ranges (e.g., various wavelengths or frequencies, or both) of electromagnetic radiation by crop or other vegetation material, including grain, in detecting crop constituents. In some examples, a crop constituent sensor may include an optical sensor, such as an optical spectrometer. In one example, a crop constituent sensor may utilize near-infrared spectroscopy or visible near-infrared spectroscopy. The crop constituent sensor can be disposed at or have access to various locations within agricultural harvester 100. For example, the crop constituent sensor can be disposed within the feeder house 106 (or otherwise have sensing access to crop material within feeder house 106) and configured to detect constituents of harvested crop material passing through the feeder house 106. In other examples, the crop constituent sensor may be located at other areas within agricultural harvester 100, for instance, on or coupled to the clean grain elevator, in a clean grain auger, or in a grain tank. In some examples, crop constituent sensors may include a capacitive sensor, which can include, for example, a capacitor for determining the dielectric properties of a crop (as well as other vegetation) material, such as dielectric properties of grain. It will be noted that these are merely examples of the types and locations of crop constituent sensors, and that various other types and locations of crop constituent sensors are contemplated.

Prior to describing how agricultural harvester 100 generates a functional predictive crop constituent map and uses the functional predictive crop constituent map for presentation or control, a brief description of some of the items on agricultural harvester 100, and their operation, will first be described. The description of FIGS. 2 and 3 describe receiving a general type of prior information map and combining information from the prior information map with a georeferenced sensor signal generated by an in-situ sensor, where the sensor signal is indicative of a characteristic in the field, such as characteristics of crop or weeds present in the field. Characteristics of the field may include, but are not limited to, characteristics of a field such as slope, weed intensity, weed type, soil moisture, surface quality; characteristics of crop properties such as crop height, crop constituents, crop density, crop state; characteristics of grain properties such as grain moisture, grain size, grain test weight, grain constituents; and characteristics of machine performance such as loss levels, job quality, fuel consumption, and power utilization. A relationship between the characteristic values obtained from in-situ sensor signals and the prior information map values is identified, and that relationship is used to generate a new functional predictive map. A functional predictive map predicts values at different geographic locations in a field, and one or more of those values may be used for controlling a machine, such as one or more subsystems of an agricultural harvester. In some instances, a functional predictive map can be presented to a user, such as an operator of an agricultural work machine, which may be an agricultural harvester. A functional predictive map may be presented to a user visually, such as via a display, haptically, or audibly. The user may interact with the functional predictive map to perform editing operations and other user interface operations. In some instances, a functional predictive map can be used for one or more of controlling an agricultural work machine, such as an agricultural harvester, presentation to an operator or other user, and presentation to an operator or user for interaction by the operator or user.

After the general approach is described with respect to FIGS. 2 and 3, a more specific approach for generating a functional predictive crop constituent map that can be presented to an operator or user, or used to control agricultural harvester 100, or both is described with respect to FIGS. 4 and 5. Again, while the present discussion proceeds with respect to the agricultural harvester and, particularly, a combine harvester, the scope of the present disclosure encompasses other types of agricultural harvesters or other agricultural work machines.

FIG. 2 is a block diagram showing some portions of an example agricultural harvester 100. FIG. 2 shows that agricultural harvester 100 illustratively includes one or more processors or servers 201, data store 202, geographic position sensor 204, communication system 206, and one or more in-situ sensors 208 that sense one or more agricultural characteristics of a field concurrent with a harvesting operation. An agricultural characteristic can include any characteristic that can have an effect of the harvesting operation. Some examples of agricultural characteristics include characteristics of the harvesting machine, the field, the plants on the field, and weather. Other types of agricultural characteristics are also included. The in-situ sensors 208 generate values corresponding to the sensed characteristics. The agricultural harvester 100 also includes a predictive model or relationship generator (collectively referred to hereinafter as “predictive model generator 210”), predictive map generator 212, control zone generator 213, control system 214, one or more controllable subsystems 216, and an operator interface mechanism 218. The agricultural harvester 100 can also include a wide variety of other agricultural harvester functionality 220. The in-situ sensors 208 include, for example, on-board sensors 222, remote sensors 224, and other sensors 226 that sense characteristics of a field during the course of an agricultural operation. Predictive model generator 210 illustratively includes a prior information variable-to-in-situ variable model generator 228, and predictive model generator 210 can include other items 230. Control system 214 includes communication system controller 229, operator interface controller 231, a settings controller 232, path planning controller 234, feed rate controller 236, header and reel controller 238, draper belt controller 240, deck plate position controller 242, residue system controller 244, machine cleaning controller 245, zone controller 247, and control system 214 can include other items 246. Controllable subsystems 216 include machine and header actuators 248, propulsion subsystem 250, steering subsystem 252, residue subsystem 138, machine cleaning subsystem 254, and controllable subsystems 216 can include a wide variety of other subsystems 256.

FIG. 2 also shows that agricultural harvester 100 can receive one or more prior information map(s) 258. As described below, the prior information map(s) include, for example, an agricultural characteristic map, such as vegetative index map, a prior operation map, a historical crop constituent map, or a soil property map. However, prior information map(s) 258 may also encompass other types of data that were obtained prior to a harvesting operation or a map from a prior operation, such as historical crop constituent data obtained prior to a harvesting operation or historical crop constituent maps from past years that contain contextual information associated with the historical crop constituent characteristic. Contextual information can include, without limitation, one or more of weather conditions over a growing season, presence of pests, geographic location, soil properties, including soil composition, such as nitrogen levels, irrigation, substance application, etc. Weather conditions can include, without limitation, precipitation over the season, presence of high winds, temperature over the season, etc. Some examples of pests broadly include, insects, fungi, weeds, bacteria, viruses, etc. Some examples of substance applications include herbicide, pesticide, fungicide, fertilizer, mineral supplements, etc. FIG. 2 also shows that an operator 260 may operate the agricultural harvester 100. The operator 260 interacts with operator interface mechanisms 218. In some examples, operator interface mechanisms 218 may include joysticks, levers, a steering wheel, linkages, pedals, buttons, dials, keypads, user actuatable elements (such as icons, buttons, etc.) on a user interface display device, a microphone and speaker (where speech recognition and speech synthesis are provided), among a wide variety of other types of control devices. Where a touch sensitive display system is provided, operator 260 may interact with operator interface mechanisms 218 using touch gestures. These examples described above are provided as illustrative examples and are not intended to limit the scope of the present disclosure. Consequently, other types of operator interface mechanisms 218 may be used and are within the scope of the present disclosure.

Prior information map 258 may be downloaded onto agricultural harvester 100 and stored in data store 202, using communication system 206 or in other ways. In some examples, communication system 206 may be a cellular communication system, a system for communicating over a wide area network or a local area network, a system for communicating over a near field communication network, or a communication system configured to communicate over any of a variety of other networks or combinations of networks. Communication system 206 may also include a system that facilitates downloads or transfers of information to and from a secure digital (SD) card or a universal serial bus (USB) card, or both.

Geographic position sensor 204 illustratively senses or detects the geographic position or location of agricultural harvester 100. Geographic position sensor 204 can include, but is not limited to, a global navigation satellite system (GNSS) receiver that receives signals from a GNSS satellite transmitter. Geographic position sensor 204 can also include a real-time kinematic (RTK) component that is configured to enhance the precision of position data derived from the GNSS signal. Geographic position sensor 204 can include a dead reckoning system, a cellular triangulation system, or any of a variety of other geographic position sensors.

In-situ sensors 208 may be any of the sensors described above with respect to FIG. 1. In-situ sensors 208 include on-board sensors 222 that are mounted on-board agricultural harvester 100. Such sensors may include, for instance, an impact plate sensor, a radiation attenuation sensor, or an image sensor that is internal to agricultural harvester 100 (such as a clean grain camera). The in-situ sensors 208 may also include remote in-situ sensors 224 that capture in-situ information. In-situ data include data taken from a sensor on-board the agricultural harvester or taken by any sensor where the data are detected during the harvesting operation. Some other examples of in-situ sensors are shown in FIG. 6B.

After being retrieved by agricultural harvester 100, prior information map selector 209 can filter or select one or more specific prior information map(s) 258 for usage by predictive model generator 210. In one example, prior information map selector 209 selects a map based on a comparison of the contextual information in the prior information map versus the present contextual information. For example, a historical crop constituent map may be selected from one of the past years where substance application characteristics were similar to the present year's substance application characteristics. Or, for example, a historical crop constituent map may be selected from one of the past years when the contextual information is not similar. For example, a historical crop constituent map may be selected for a prior year that was “dry” (i.e., had drought conditions or reduced precipitation), while the present year is “wet” (i.e., had increased precipitation or flood conditions). There still may be a useful historical relationship, but the relationship may be inverse. For instance, areas of the field that had low levels of one or more crop constituents in a dry year may be areas of higher crop constituents in a wet year as these areas may provide better growing conditions in a wet year. Present contextual information may include contextual information beyond immediate contextual information. For instance, present contextual information can include, but not by limitation, a set of information corresponding to the present growing season, a set of data corresponding to a winter before the current growing season, or a set of data corresponding to several past years, amongst others.

The contextual information can also be used for correlations between areas with similar contextual characteristics, regardless of whether the geographic position corresponds to the same position on prior information map 258. For instance, historical crop constituent values from area with similar agricultural characteristics, such as vegetative index values, substance application characteristics, soil properties, such assoil composition characteristics, as well as various other agricultural characteristics, in other fields can be used as prior information map 258 to create the predictive crop characteristic map. For example, the contextual characteristic information associated with a different location may be applied to the location on the prior information map 258 having similar characteristic information.

Predictive model generator 210 generates a model that is indicative of a relationship between the values sensed by the in-situ sensor 208 and a characteristic mapped to the field by the prior information map 258. For example, if the prior information map 258 maps an agricultural characteristic value to different locations in the field, and the in-situ sensor 208 is sensing a value indicative of crop constituents, then prior information variable-to-in-situ variable model generator 228 generates a predictive crop constituent model that models the relationship between the agricultural characteristic values and the crop constituent values. Then, predictive map generator 212 uses the predictive crop constituent model generated by predictive model generator 210 to generate a functional predictive crop constituent map that predicts the value of crop constituents, at different locations in the field, based upon the prior information map 258. Or, for example, if the prior information map 258 maps a vegetative index value to different locations in the field, and the in-situ sensor 208 is sensing a value indicative of crop constituents, then prior information variable-to-in-situ variable model generator 228 generates a predictive crop constituent model that models the relationship between the vegetative index values and the crop constituent values. Then, predictive map generator 212 uses the predictive crop constituent model generated by predictive model generator 210 to generate a functional predictive crop constituent map that predicts the value of crop constituents, at different locations in the field, based upon the prior information map 258. Or, for example, if the prior information map 258 maps a historical crop constituent value to different locations in the field and the in-situ sensor 208 is sensing a value indicative of crop constituents, then prior information variable-to-in-situ variable model generator 228 generates a predictive crop constituent model that models the relationship between the historical crop constituent values (with or without contextual information) and the in-situ crop constituent values. Then, predictive map generator 212 uses the predictive crop constituent model generated by predictive model generator 210 to generate a functional predictive crop constituent map that predicts the value of crop constituents, at different locations in the field, based upon the prior information map 258. Or, for example, if the prior information map 258 maps a prior operation characteristic value (such as a substance application characteristic) to different locations in the field, and the in-situ sensor 208 is sensing a value indicative of crop constituents, then prior information variable-to-in-situ variable model generator 228 generates a predictive crop constituent model that models the relationship between the prior operation characteristic values and the crop constituent values. Then, predictive map generator 212 uses the predictive crop constituent model generated by predictive model generator 210 to generate a functional predictive crop constituent map that predicts the value of crop constituents, at different locations in the field, based upon the prior information map 258. Or, for example, if the prior information map 258 maps a soil property value, such as a soil composition value (such as a nitrogen value), to different locations in the field, and the in-situ sensor 208 is sensing a value indicative of crop constituents, then prior information variable-to-in-situ variable model generator 228 generates a predictive crop constituent model that models the relationship between the soil property values, such as the soil composition values, and the crop constituent values. Then, predictive map generator 212 uses the predictive crop constituent model generated by predictive model generator 210 to generate a functional predictive crop constituent map that predicts the value of crop constituents, at different locations in the field, based upon the prior information map 258.

In some examples, the type of data in the functional predictive map 263 may be the same as the in-situ data type sensed by the in-situ sensors 208. In some instances, the type of data in the functional predictive map 263 may have different units from the data sensed by the in-situ sensors 208. In some examples, the type of data in the functional predictive map 263 may be different from the data type sensed by the in-situ sensors 208 but has a relationship to data type sensed by the in-situ sensors 208. For example, in some examples, the in-situ data type may be indicative of the type of data in the functional predictive map 263. In some examples, the type of data in the functional predictive map 263 may be different than the data type in the prior information map 258. In some instances, the type of data in the functional predictive map 263 may have different units from the data in the prior information map 258. In some examples, the type of data in the functional predictive map 263 may be different from the data type in the prior information map 258 but has a relationship to the data type in the prior information map 258. For example, in some examples, the data type in the prior information map 258 may be indicative of the type of data in the functional predictive map 263. In some examples, the type of data in the functional predictive map 263 is different than one of, or both of the in-situ data type sensed by the in-situ sensors 208 and the data type in the prior information map 258. In some examples, the type of data in the functional predictive map 263 is the same as one of, or both of, of the in-situ data type sensed by the in-situ sensors 208 and the data type in prior information map 258. In some examples, the type of data in the functional predictive map 263 is the same as one of the in-situ data type sensed by the in-situ sensors 208 or the data type in the prior information map 258, and different than the other.

Continuing with the preceding examples, predictive map generator 212 can use the values in prior information map 258 and the model generated by predictive model generator 210 to generate a functional predictive map 263 that predicts crop constituents at different locations in the field. Predictive map generator 212 thus outputs predictive map 264.

As shown in FIG. 2, predictive map 264 predicts the value of a characteristic, which may be the same characteristic sensed by in-situ sensor(s) 208, or a characteristic related to the characteristic sensed by the in-situ sensor(s) 208, at various locations across the field based upon a prior information value in prior information map 258 at those locations (or locations with similar contextual information, even if in a different field) and using the predictive model. For example, if predictive model generator 210 has generated a predictive model indicative of a relationship between an agricultural characteristic value and a crop constituent value, then, given the agricultural characteristic value at different locations across the field, predictive map generator 212 generates a predictive map 264 that predicts the crop constituent value at different locations across the field. The agricultural characteristic value, obtained from the prior information map 258, at those locations and the relationship between the agricultural characteristic value and the crop constituent value, obtained from the predictive model, are used to generate the predictive map 264.

Or, for example, if predictive model generator 210 has generated a predictive model indicative of a relationship between a vegetative index value and a crop constituent value, then, given the vegetative index value at different locations across the field, predictive map generator 212 generates a predictive map 264 that predicts the crop constituent value at different locations across the field. The vegetative index value, obtained from the prior information map 258, at those locations and the relationship between vegetative index values and crop constituent values, obtained from the predictive model, are used to generate the predictive map 264.

Or, for example, if predictive model generator 210 has generated a predictive model indicative of a relationship between a historical crop constituent value and a crop constituent value detected in-situ, then, given the historical crop constituent value at different locations across the field, predictive map generator 212 generates a predictive map 264 that predicts the value of the crop constituent at different locations across the field. The historical crop constituent value, obtained from the prior information map 258, at those locations and the relationship between historical crop constituent values and in-situ crop constituent values, obtained from the predictive model, are used to generate the predictive map 264.

Or, for example, if predictive model generator 210 has generated a predictive model indicative of a relationship between a prior operation characteristic value (such as a substance application characteristic value) and a crop constituent value, then, given the prior operation characteristic value at different locations across the field, predictive map generator 212 generates a predictive map 264 that predicts the crop constituent value at different locations across the field. The prior operation characteristic value, obtained from the prior information map 258, at those locations and the relationship between prior operation characteristic values and crop constituent values, obtained from the predictive model, are used to generate the predictive map 264.

Or, for example, if predictive model generator 210 has generated a predictive model indicative of a relationship between a soil property value, such as soil composition value (such as a nitrogen level value), and a crop constituent value, then, given the soil property value, such as the soil composition value, at different locations across the field, predictive map generator 212 generates a predictive map 264 that predicts the crop constituent value at different locations across the field. The soil property value, such as the soil composition value, obtained from the prior information map 258, at those locations and the relationship between the soil property value, such as the soil composition value, and the crop constituent value, obtained from the predictive model, are used to generate the predictive map 264.

Some variations in the data types that are mapped in the prior information map 258, the data types sensed by in-situ sensors 208 and the data types predicted on the predictive map 264 will now be described.

In some examples, the data type in the prior information map 258 is different from the data type sensed by in-situ sensors 208, yet the data type in the predictive map 264 is the same as the data type sensed by the in-situ sensors 208. For instance, the prior information map 258 may be a vegetative index map, and the variable sensed by the in-situ sensors 208 may be crop constituents. The predictive map 264 may then be a predictive crop constituent map that maps predicted crop constituent values to different geographic locations in the field. predictive map 264

Also, in some examples, the data type in the prior information map 258 is different from the data type sensed by in-situ sensors 208, and the data type in the predictive map 264 is different from both the data type in the prior information map 258 and the data type sensed by the in-situ sensors 208. For instance, the prior information map 258 may be a vegetative index map, and the variable sensed by the in-situ sensors 208 may be crop constituent. The predictive map 264 may then be a predictive biomass map that maps predicted biomass values to different geographic locations in the field. In another example, the prior information map 258 may be a vegetative index map, and the variable sensed by the in-situ sensors 208 may be crop constituent. The predictive map 264 may then be a predictive route map that maps a predicted harvester travel path along the field.

In some examples, the prior information map 258 is from a prior pass through the field during a prior operation and the data type is different from the data type sensed by in-situ sensors 208, yet the data type in the predictive map 264 is the same as the data type sensed by the in-situ sensors 208. For instance, the prior information map 258 may be a substance application map, such as a fertilizer application map, generated during a substance application operation, and the variable sensed by the in-situ sensors 208 may be crop constituent characteristics. The predictive map 264 may then be a predictive crop constituent map that maps predicted crop constituent values to different geographic locations in the field. predictive map 264

In some examples, the prior information map 258 is from a prior pass through the field during a prior operation and the data type is the same as the data type sensed by in-situ sensors 208, and the data type in the predictive map 264 is also the same as the data type sensed by the in-situ sensors 208. For instance, the prior information map 258 may be a crop constituent map generated during a previous year, and the variable sensed by the in-situ sensors 208 may be crop constituents. The predictive map 264 may then be a predictive crop constituent map that maps predicted crop constituent values to different geographic locations in the field. In such an example, the relative crop constituent differences in the georeferenced prior information map 258 from the prior year can be used by predictive model generator 210 to generate a predictive model that models a relationship between the relative crop constituent differences on the prior information map 258 and the crop constituent values sensed by in-situ sensors 208 during the current harvesting operation. The predictive model is then used by predictive map generator 212 to generate a predictive crop constituent map.

In some examples, predictive map 264 can be provided to the control zone generator 213. Control zone generator 213 groups adjacent portions of an area into one or more control zones based on data values of predictive map 264 that are associated with those adjacent portions. A control zone may include two or more contiguous portions of an area, such as a field, for which a control parameter corresponding to the control zone for controlling a controllable subsystem is constant. For example, a response time to alter a setting of controllable subsystems 216 may be inadequate to satisfactorily respond to changes in values contained in a map, such as predictive map 264. In that case, control zone generator 213 parses the map and identifies control zones that are of a defined size to accommodate the response time of the controllable subsystems 216. In another example, control zones may be sized to reduce wear from excessive actuator movement resulting from continuous adjustment. In some examples, there may be a different set of control zones for each controllable subsystem 216 or for groups of controllable subsystems 216. The control zones may be added to the predictive map 264 to obtain predictive control zone map 265. Predictive control zone map 265 can thus be similar to predictive map 264 except that predictive control zone map 265 includes control zone information defining the control zones. Thus, a functional predictive map 263, as described herein, may or may not include control zones. Both predictive map 264 and predictive control zone map 265 are functional predictive maps 263. In one example, a functional predictive map 263 does not include control zones, such as predictive map 264. In another example, a functional predictive map 263 does include control zones, such as predictive control zone map 265. In some examples, multiple crops may be simultaneously present in a field if an intercrop production system is implemented. In that case, predictive map generator 212 and control zone generator 213 are able to identify the location and characteristics of the two or more crops and then generate predictive map 264 and predictive control zone map 265 accordingly.

It will also be appreciated that control zone generator 213 can cluster values to generate control zones and the control zones can be added to predictive control zone map 265, or a separate map, showing only the control zones that are generated. In some examples, the control zones may be used for controlling or calibrating agricultural harvester 100 or both. In other examples, the control zones may be presented to the operator 260 and used to control or calibrate agricultural harvester 100, and, in other examples, the control zones may be presented to the operator 260 or another user or stored for later use.

Predictive map 264 or predictive control zone map 265 or both are provided to control system 214, which generates control signals based upon the predictive map 264 or predictive control zone map 265 or both. In some examples, communication system controller 229 controls communication system 206 to communicate the predictive map 264 or predictive control zone map 265 or control signals based on the predictive map 264 or predictive control zone map 265 to other agricultural harvesters that are harvesting in the same field. In some examples, communication system controller 229 controls the communication system 206 to send the predictive map 264, predictive control zone map 265, or both to other remote systems.

Operator interface controller 231 is operable to generate control signals to control operator interface mechanisms 218. The operator interface controller 231 is also operable to present the predictive map 264 or predictive control zone map 265 or other information derived from or based on the predictive map 264, predictive control zone map 265, or both to operator 260. Operator 260 may be a local operator or a remote operator. As an example, controller 231 generates control signals to control a display mechanism to display one or both of predictive map 264 and predictive control zone map 265 for the operator 260. Controller 231 may generate operator actuatable mechanisms that are displayed and can be actuated by the operator to interact with the displayed map. The operator can edit the map by, for example, correcting a crop constituent value displayed on the map based on the operator's observation. Settings controller 232 can generate control signals to control various settings on the agricultural harvester 100 based upon predictive map 264, the predictive control zone map 265, or both. For instance, settings controller 232 can generate control signals to control machine and header actuators 248. In response to the generated control signals, the machine and header actuators 248 operate to control, for example, one or more of the sieve and chaffer settings, concave clearance, rotor settings, cleaning fan speed settings, header height, header functionality, reel speed, reel position, draper functionality (where agricultural harvester 100 is coupled to a draper header), corn header functionality, internal distribution control, and other actuators 248 that affect the other functions of the agricultural harvester 100. Path planning controller 234 illustratively generates control signals to control steering subsystem 252 to steer agricultural harvester 100 according to a desired path. Path planning controller 234 can control a path planning system to generate a route for agricultural harvester 100 and can control propulsion subsystem 250 and steering subsystem 252 to steer agricultural harvester 100 along that route. Feed rate controller 236 can control various subsystems, such as propulsion subsystem 250 and machine actuators 248, to control a feed rate based upon the predictive map 264 or predictive control zone map 265 or both. For instance, as agricultural harvester 100 approaches an area having high protein wheat plants, as indicated by one or more crop constituent values, control system 214 may adjust the speed setting of cleaning fan 120, to adjust the speed of cleaning fan 120, to perform enhanced cleaning of the high protein wheat plant material. Header and reel controller 238 can generate control signals to control a header or a reel or other header functionality. Draper belt controller 240 can generate control signals to control a draper belt or other draper functionality based upon the predictive map 264, predictive control zone map 265, or both. Deck plate position controller 242 can generate control signals to control a position of a deck plate included on a header based on predictive map 264 or predictive control zone map 265 or both, and residue system controller 244 can generate control signals to control a residue subsystem 138 based upon predictive map 264 or predictive control zone map 265, or both. Machine cleaning controller 245 can generate control signals to control machine cleaning subsystem 254. For instance, based upon the different types of seeds or weeds passed through agricultural harvester 100, a particular type of machine cleaning operation or a frequency with which a cleaning operation is performed may be controlled. Other controllers included on the agricultural harvester 100 can control other subsystems based on the predictive map 264 or predictive control zone map 265 or both as well.

FIGS. 3A and 3B (collectively referred to herein as FIG. 3) show a flow diagram illustrating one example of the operation of agricultural harvester 100 in generating a predictive map 264 and predictive control zone map 265 based upon prior information map 258.

At 280, agricultural harvester 100 receives prior information map 258. Examples of prior information map 258 or receiving prior information map 258 are discussed with respect to blocks 281, 282, 284 and 286. As discussed above, prior information map 258 maps values of a variable, corresponding to a first characteristic, to different locations in the field, as indicated at block 282. For instance, one prior information map may be a prior operation map generated during a prior operation or based on data from a prior operation on the field, such as prior substance application operation by a substance application machine, such as a spraying operation by an agricultural sprayer or a spreading operation by an agricultural spreader. In another example, the prior information map may be a historical crop constituent map that is based on data from a prior harvesting operation. In another example, the prior information map may be a vegetative index map. In another example, the prior information map may be a soil composition map. These are merely examples. Prior information map may include any of a number of agricultural characteristic maps that map agricultural characteristic values to different geographic locations in one or more field(s) of interest. The data for the prior information map 258 may be collected in other ways as well. For instance, the data may be collected based on aerial images or measured values taken during a previous year, or earlier in the current growing season, or at other times. The information may be based on data detected or gathered in other ways (other than using aerial images) as well. For instance, the data for the prior information map 258 can be transmitted to agricultural harvester 100 using communication system 206 and stored in data store 202. The data for the prior information map 258 can be provided to agricultural harvester 100 using communication system 206 in other ways as well, and this is indicated by block 286 in the flow diagram of FIG. 3. In some examples, the prior information map 258 can be received by communication system 206.

At block 287, prior information map selector 209 can select one or more maps from the plurality of candidate prior information maps received in block 280. For example, multiple years of historical crop constituent maps may be received as candidate prior information maps. Each of these maps can contain contextual information such as weather patterns over a period of time, such as a year, pest surges over a period of time, such as a year, soil properties, topographic characteristics, substance application characteristics, etc. Contextual information can be used to select which historical crop constituent map should be selected. For instance, the weather conditions over a period of time, such in a current year, or the soil properties for the current field can be compared to the weather conditions and soil properties in the contextual information for each candidate prior information map. The results of such a comparison can be used to select which historical crop constituent map should be selected. For example, years with similar weather conditions may generally result in similar crop constituent characteristics or crop constituent trends across a field. In some cases, years with opposite weather conditions may also be useful for predicting crop constituents based on historical crop constituent data. For instance, an area with a low crop constituent value in a dry year, might have a high crop constituent value in a wet year as the area may provide for better growing conditions. The process by which one or more prior information maps are selected by prior information map selector 209 can be manual, semi-automated or automated. In some examples, during a harvesting operation, prior information map selector 209 can continually or intermittently determine whether a different prior information map has a better relationship with the in-situ sensor value. If a different prior information map is correlating with the in-situ data more closely, then prior information map selector 209 can replace the currently selected prior information map with the more correlative prior information map.

Upon commencement of a harvesting operation, in-situ sensors 208 generate sensor signals indicative of one or more in-situ data values indicative of a characteristic, such as a crop characteristic, as indicated by block 288. Examples of in-situ sensors 288 are discussed with respect to blocks 222, 290, and 226. As explained above, the in-situ sensors 208 include on-board sensors 222; remote in-situ sensors 224, such as UAV-based sensors flown at a time to gather in-situ data, shown in block 290; or other types of in-situ sensors, designated by in-situ sensors 226. In some examples, data from on-board sensors is georeferenced using position heading or speed data from geographic position sensor 204.

Predictive model generator 210 controls the prior information variable-to-in-situ variable model generator 228 to generate a model that models a relationship between the mapped values contained in the prior information map 258 and the in-situ values sensed by the in-situ sensors 208 as indicated by block 292. The characteristics or data types represented by the mapped values in the prior information map 258 and the in-situ values sensed by the in-situ sensors 208 may be the same characteristics or data type or different characteristics or data types.

The relationship or model generated by predictive model generator 210 is provided to predictive map generator 212. Predictive map generator 212 generates a predictive map 264 that predicts a value of the characteristic sensed by the in-situ sensors 208 at different geographic locations in a field being harvested, or a different characteristic that is related to the characteristic sensed by the in-situ sensors 208, using the predictive model and the prior information map 258, as indicated by block 294.

It should be noted that, in some examples, the prior information map 258 may include two or more different maps or two or more different map layers of a single map. Each map layer may represent a different data type from the data type of another map layer or the map layers may have the same data type that were obtained at different times. Each map in the two or more different maps or each layer in the two or more different map layers of a map maps a different type of variable to the geographic locations in the field. In such an example, predictive model generator 210 generates a predictive model that models the relationship between the in-situ data and each of the different variables mapped by the two or more different maps or the two or more different map layers. Similarly, the in-situ sensors 208 can include two or more sensors each sensing a different type of variable. Thus, the predictive model generator 210 generates a predictive model that models the relationships between each type of variable mapped by the prior information map 258 and each type of variable sensed by the in-situ sensors 208. Predictive map generator 212 can generate a functional predictive map 263 that predicts a value for each sensed characteristic sensed by the in-situ sensors 208 (or a characteristic related to the sensed characteristic) at different locations in the field being harvested using the predictive model and each of the maps or map layers in the prior information map 258.

Predictive map generator 212 configures the predictive map 264 so that the predictive map 264 is actionable (or consumable) by control system 214. Predictive map generator 212 can provide the predictive map 264 to the control system 214 or to control zone generator 213 or both. Some examples of different ways in which the predictive map 264 can be configured or output are described with respect to blocks 296, 295, 299 and 297. For instance, predictive map generator 212 configures predictive map 264 so that predictive map 264 includes values that can be read by control system 214 and used as the basis for generating control signals for one or more of the different controllable subsystems of the agricultural harvester 100, as indicated by block 296.

Control zone generator 213 can divide the predictive map 264 into control zones based on the values on the predictive map 264. Contiguously-geolocated values that are within a threshold value of one another can be grouped into a control zone. The threshold value can be a default threshold value, or the threshold value can be set based on an operator input, based on an input from an automated system or based on other criteria. A size of the zones may be based on a responsiveness of the control system 214, the controllable subsystems 216, or based on wear considerations, or on other criteria as indicated by block 295. Predictive map generator 212 configures predictive map 264 for presentation to an operator or other user. Control zone generator 213 can configure predictive control zone map 265 for presentation to an operator or other user. This is indicated by block 299. When presented to an operator or other user, the presentation of the predictive map 264 or predictive control zone map 265 or both may contain one or more of the predictive values on the predictive map 264 correlated to geographic location, the control zones on predictive control zone map 265 correlated to geographic location, and settings values or control parameters that are used based on the predicted values on predictive map 264 or zones on predictive control zone map 265. The presentation can, in another example, include more abstracted information or more detailed information. The presentation can also include a confidence level that indicates an accuracy with which the predictive values on predictive map 264 or the zones on predictive control zone map 265 conform to measured values that may be measured by sensors on agricultural harvester 100 as agricultural harvester 100 moves through the field. Further where information is presented to more than one location, an authentication/authorization system can be provided to implement authentication and authorization processes. For instance, there may be a hierarchy of individuals that are authorized to view and change maps and other presented information. By way of example, an on-board display device may show the maps in near real time locally on the machine, only, or the maps may also be generated at one or more remote locations. In some examples, each physical display device at each location may be associated with a person or a user permission level. The user permission level may be used to determine which display markers are visible on the physical display device, and which values the corresponding person may change. As an example, a local operator of agricultural harvester 100 may be unable to see the information corresponding to the predictive map 264 or make any changes to machine operation. A supervisor, at a remote location, however, may be able to see the predictive map 264 on the display, but not make changes. A manager, who may be at a separate remote location, may be able to see all of the elements on predictive map 264 and also change the predictive map 264 that is used in machine control. This is one example of an authorization hierarchy that may be implemented. The predictive map 264 or predictive control zone map 265 or both can be configured in other ways as well, as indicated by block 297.

At block 298, input from geographic position sensor 204 and other in-situ sensors 208 are received by the control system. Block 300 represents receipt by control system 214 of an input from the geographic position sensor 204 identifying a geographic location of agricultural harvester 100. Block 302 represents receipt by the control system 214 of sensor inputs indicative of trajectory or heading of agricultural harvester 100, and block 304 represents receipt by the control system 214 of a speed of agricultural harvester 100. Block 306 represents receipt by the control system 214 of other information from various in-situ sensors 208.

At block 308, control system 214 generates control signals to control the controllable subsystems 216 based on the predictive map 264 or predictive control zone map 265 or both and the input from the geographic position sensor 204 and any other in-situ sensors 208. At block 310, control system 214 applies the control signals to the controllable subsystems. It will be appreciated that the particular control signals that are generated, and the particular controllable subsystems 216 that are controlled, may vary based upon one or more different things. For example, the control signals that are generated and the controllable subsystems 216 that are controlled may be based on the type of predictive map 264 or predictive control zone map 265 or both that is being used. Similarly, the control signals that are generated and the controllable subsystems 216 that are controlled and the timing of the control signals can be based on various latencies of crop flow through the agricultural harvester 100 and the responsiveness of the controllable subsystems 216.

By way of example, a generated predictive map 264 in the form of a predictive crop constituent map can be used to control one or more controllable subsystems 216. For example, the functional predictive crop constituent map can include crop constituent values georeferenced to locations within the field being harvested. The functional predictive crop constituent map can be extracted and used to control the steering and propulsion subsystems 252 and 250, respectively. By controlling the steering and propulsion subsystems 252 and 250, a feed rate of material or grain moving through the agricultural harvester 100 can be controlled. Similarly, the header height can be controlled to take in more or less material and thus the header height can also be controlled to control feed rate of material through the agricultural harvester 100. In other examples, if the predictive map 264 maps a crop constituent value forward of the machine being higher on one portion of the header than another portion of the header, resulting in a different biomass entering one side of the header than the other side, control of the header may be implemented. For example, a draper speed on one side of the header may be increased or decreased relative to the draper speed other side of the header to account for the additional biomass. Thus, the header and reel controller 238 can be controlled using georeferenced values present in the predictive crop constituent map to control draper speeds of the draper belts on the header. The preceding example involving feed rate and header control using a functional predictive crop constituent map is provided merely as an example. Consequently, a wide variety of other control signals can be generated using values obtained from a predictive crop constituent map or other type of functional predictive map to control one or more of the controllable subsystems 216.

At block 312, a determination is made as to whether the harvesting operation has been completed. If harvesting is not completed the processing advances to block 314 where in-situ sensor data from geographic position sensor 204 and in-situ sensors 208 (and perhaps other sensors) continue to be read.

In some examples, at block 316, agricultural harvester 100 can also detect learning trigger criteria to perform machine learning on one or more of the predictive map 264, predictive control zone map 265, the model generated by predictive model generator 210, the zones generated by control zone generator 213, one or more control algorithms implemented by the controllers in the control system 214, and other triggered learning.

The learning trigger criteria can include any of a wide variety of different criteria. Some examples of detecting trigger criteria are discussed with respect to blocks 318, 320, 321, 322 and 324. For instance, in some examples, triggered learning can involve recreation of a relationship used to generate a predictive model when a threshold amount of in-situ sensor data are obtained from in-situ sensors 208. In such examples, receipt of an amount of in-situ sensor data from the in-situ sensors 208 that exceeds a threshold triggers or causes the predictive model generator 210 to generate a new predictive model that is used by predictive map generator 212. Thus, as agricultural harvester 100 continues a harvesting operation, receipt of the threshold amount of in-situ sensor data from the in-situ sensors 208 triggers the creation of a new relationship represented by a predictive model generated by predictive model generator 210. Further, new predictive map 264, predictive control zone map 265, or both can be regenerated using the new predictive model. Block 318 represents detecting a threshold amount of in-situ sensor data used to trigger creation of a new predictive model.

In other examples, the learning trigger criteria may be based on how much the in-situ sensor data from the in-situ sensors 208 are changing, such as over time or compared to previous values. For example, if variations within the in-situ sensor data (or the relationship between the in-situ sensor data and the information in prior information map 258) are within a selected range or is less than a defined amount or is below a threshold value, then a new predictive model is not generated by the predictive model generator 210. As a result, the predictive map generator 212 does not generate a new predictive map 264, predictive control zone map 265, or both. However, if variations within the in-situ sensor data are outside of the selected range, are greater than the defined amount, or are above the threshold value, for example, then the predictive model generator 210 generates a new predictive model using all or a portion of the newly received in-situ sensor data that the predictive map generator 212 uses to generate a new predictive map 264. At block 320, variations in the in-situ sensor data, such as a magnitude of an amount by which the data exceeds the selected range or a magnitude of the variation of the relationship between the in-situ sensor data and the information in the prior information map 258, can be used as a trigger to cause generation of a new predictive model and predictive map. Keeping with the examples described above, the threshold, the range, and the defined amount can be set to default values; set by an operator or user interaction through a user interface; set by an automated system; or set in other ways.

Other learning trigger criteria can also be used. For instance, if predictive model generator 210 switches to a different prior information map (different from the originally selected prior information map 258), then switching to the different prior information map may trigger re-learning by predictive model generator 210, predictive map generator 212, control zone generator 213, control system 214, or other items. In another example, transitioning of agricultural harvester 100 to a different topography or to a different control zone may be used as learning trigger criteria as well.

In some instances, operator 260 can also edit the predictive map 264 or predictive control zone map 265 or both. The edits can change a value on the predictive map 264; change a size, shape, position, or existence of a control zone on predictive control zone map 265; or both. Block 321 shows that edited information can be used as learning trigger criteria.

In some instances, it may also be that operator 260 observes that automated control of a controllable subsystem, is not what the operator desires. In such instances, the operator 260 may provide a manual adjustment to the controllable subsystem reflecting that the operator 260 desires the controllable subsystem to operate in a different way than is being commanded by control system 214. Thus, manual alteration of a setting by the operator 260 can cause one or more of predictive model generator 210 to relearn a model, predictive map generator 212 to regenerate map 264, control zone generator 213 to regenerate one or more control zones on predictive control zone map 265, and control system 214 to relearn a control algorithm or to perform machine learning on one or more of the controller components 232 through 246 in control system 214 based upon the adjustment by the operator 260, as shown in block 322. Block 324 represents the use of other triggered learning criteria.

In other examples, relearning may be performed periodically or intermittently based, for example, upon a selected time interval such as a discrete time interval or a variable time interval, as indicated by block 326.

If relearning is triggered, whether based upon learning trigger criteria or based upon passage of a time interval, as indicated by block 326, then one or more of the predictive model generator 210, predictive map generator 212, control zone generator 213, and control system 214 performs machine learning to generate a new predictive model, a new predictive map, a new control zone, and a new control algorithm, respectively, based upon the learning trigger criteria. The new predictive model, the new predictive map, and the new control algorithm are generated using any additional data that has been collected since the last learning operation was performed. Performing relearning is indicated by block 328.

If the harvesting operation has been completed, operation moves from block 312 to block 330 where one or more of the predictive map 264, predictive control zone map 265, and predictive model generated by predictive model generator 210 are stored. The predictive map 264, predictive control zone map 265, and predictive model may be stored locally on data store 202 or sent to a remote system using communication system 206 for later use.

It will be noted that while some examples herein describe predictive model generator 210 and predictive map generator 212 receiving a prior information map in generating a predictive model and a functional predictive map, respectively, in other examples, the predictive model generator 210 and predictive map generator 212 can receive, in generating a predictive model and a functional predictive map, respectively, other types of maps, including predictive maps, such as a functional predictive map generated during the harvesting operation. For example, predictive model generator 210 and predictive map generator 212 can receive a predictive map, such as a predictive biomass map, a predictive yield map, or a predictive crop moisture map.

FIG. 4 is a block diagram of a portion of the agricultural harvester 100 shown in FIG. 1. Particularly, FIG. 4 shows, among other things, examples of the predictive model generator 210 and the predictive map generator 212 in more detail. FIG. 4 also illustrates information flow among the various components shown therein. As shown, the predictive model generator 210 receives one or more of a vegetative index map 332, a historical crop constituent map 333, a prior operation map 341, a soil property map 343, a predictive map 353, such as a predictive biomass map, a predictive crop moisture map, or a predictive yield map, or an agricultural characteristic map 347. In some examples, the predictive map (such as the predictive biomass map, the predictive yield map, or the predictive crop moisture map) are generated by the process described in FIG. 3. In other examples, predictive model generator 210 may receive various other maps 401. Historical crop constituent map 333 includes historical crop constituent values 335 indicative of crop constituent values across the field during a past harvest. Historical crop constituent map 333 also includes contextual data 337 that is indicative of the context or conditions that may have influenced the crop constituent value for the past year(s). For example, contextual data 337 can include soil properties, such as soil type, soil moisture, soil cover, or soil structure, soil composition (such as nitrogen content), topographic characteristics, such as elevation or slope, plant date, harvest date, substance application (such as fertilizer application) characteristics, seed genotype (hybrids, cultivar, etc.), a measure of weed presence, a measure of pest presence, weather conditions, e.g., rainfall, snow coverage, hail, wind, temperature, etc. Historical crop constituent map 333 can include other items as well, as indicated by block 339. As shown in the illustrated example, vegetative index map 332, prior operation map 341, soil composition map 343, predictive map 353, and agricultural characteristic map 347 do not contain additional information. However, in other examples, vegetative index map 332, prior operation map 341, soil composition map 343, predictive map 353, and agricultural characteristic map 347 can include other items as well. As an example, weed growth has an effect on a vegetative index reading. Consequently, herbicide application in temporal relation to the vegetative index sensing used to generate vegetative index map 332 may be contextual information included in the vegetative index map 332 to provide context to the vegetative index values. Various other types of information can also be included with vegetative index map 332, topographic map 341, or soil property map 343.

Besides receiving one or more of a vegetative index map 332, a historical crop constituent map 333, a prior operation map 341, a soil composition map 343, a predictive map 353, or an agricultural characteristic map 347 as a prior information map, predictive model generator 210 also receives a geographic location 334, or an indication of a geographic location, from geographic position sensor 204. In-situ sensors 208 illustratively include a crop constituent sensor 336 as well as a processing system 338. The processing system 338 processes sensor data generated from the crop constituent sensors 336. In some examples, crop constituent sensor 336 may be on-board agricultural harvester 100.

In some examples, crop constituent sensors 336 may utilize one or more bands of electromagnetic radiation in detecting crop constituents. For example, a crop constituent sensor 336 may utilize the reflectance or absorption of various ranges (e.g., various wavelengths or frequencies, or both) of electromagnetic radiation by crop or other vegetation material, including grain of crop plants, in detecting crop constituents. In some examples, a crop constituent sensor may include an optical sensor, such as an optical spectrometer. In one example, a crop constituent sensor 336 may utilize near-infrared spectroscopy or visible and near-infrared spectroscopy. The crop constituent sensor 336 can be disposed at or have access to various locations within agricultural harvester 100. For example, the crop constituent sensor 336 can be disposed within the feeder house 106 (or otherwise have sensing access to crop material within feeder house 106) and configured to detect constituents of harvested crop material passing through the feeder house 106. In other examples, the crop constituent sensor 336 may be located at other areas within agricultural harvester 100, for instance, on, coupled to, or disposed within the clean grain elevator, on, coupled to, or disposed within a clean grain auger, or on, coupled to, or disposed within a grain tank. It will be noted that these are merely examples of the types and locations of crop constituent sensors 336, and that various other types and locations of crop constituent sensors 336 are contemplated.

Processing system 338 processes one or more sensor signals generated by the crop constituent sensor 336 to generate processed sensor data identifying one or more crop constituent values. Processing system 338 can also geolocate the values received from the in-situ sensor 208. For example, the location of the agricultural harvester at the time a signal from in-situ sensor 208 is received may not be the accurate location of the crop constituent value. This is because an amount of time may elapse between when the agricultural harvester makes initial contact with the crop plant and when the crop plant is sensed by the crop constituent sensor 336, or other in-situ sensor 208. Thus, a transient time between when a plant is initially encountered and when the plant is sensed within the agricultural harvester is taken into account when georeferencing the sensed data. By doing so, the crop constituent value can be accurately georeferenced to the correct location on the field. For example, due to travel of severed crop along a header in a direction that is transverse to a direction of travel of the agricultural harvester, the crop constituent values may geolocate to a chevron shape area rearward of the agricultural harvester as the agricultural harvester travels in a forward direction.

Processing system 338 allocates or apportions an aggregate crop constituent value detected by a crop constituent sensor during each time or measurement interval back to earlier geo-referenced regions based upon the travel times of the crop from different portions of the agricultural harvester, such as different lateral locations along a width of a header of the agricultural harvester and the ground speed of the harvester. For example, processing system 338 allocates a measured aggregate crop constituent value from a measurement interval or time back to geo-referenced regions that were traversed by a header of the agricultural harvester during different measurement intervals or times. The processing system 338 apportions or allocates the aggregate crop constituent value from a particular measurement interval or time to previously traversed geo-referenced regions which are part of the chevron shape area.

In some examples, crop constituent sensor 336 can rely on different types of radiation and the way in which radiation is reflected by, absorbed by, attenuated by, or transmitted through the crop material, including grain. The crop constituent sensor 336 may sense other electromagnetic properties of crop material such as electrical permittivity when the material passes between two capacitive plates. Other material properties and sensors may also be used. In some examples, raw or processed data from crop constituent sensor 336 may be presented to operator 260 via operator interface mechanism 218. Operator 260 may be onboard of the work agricultural harvester 100 or at a remote location.

The present discussion proceeds with respect to an example in which crop constituent sensor 336 detects a crop constituent value. It will be appreciated that this is merely one example, and the sensors mentioned above, as other examples of crop constituent sensor 336, are contemplated herein as well. As shown in FIG. 4, the predictive model generator 210 includes a vegetative index-to-crop constituent model generator 342, a historical crop constituent-to-crop constituent model generator 344, a soil composition-to-crop constituent model generator 345, a prior operation characteristic-to-crop constituent model generator 346, a predictive characteristic-to-crop constituent model generator 347, and an agricultural characteristic-to-crop constituent model generator 348. In other examples, the predictive model generator 210 may include additional, fewer, or different components than those shown in the example of FIG. 4. Consequently, in some examples, the predictive model generator 210 may include other items 349 as well, which may include other types of predictive model generators to generate other types of crop constituent models. For example, other model generators 348 may include specific characteristics, for example, specific vegetative index characteristics, such as crop growth or crop health; specific soil composition characteristics, such as nitrogen levels, specific prior operation characteristics such as substance application characteristics from a prior substance application operation; specific agricultural characteristics; specific predictive characteristics, such as predictive biomass, predictive yield, or predictive crop moisture, or specific crop constituents, such as oil, starch, protein, as well as various other crop constituents.

Vegetative index-to-crop constituent model generator 342 identifies a relationship between in-situ crop constituent data 340 at a geographic location corresponding to where in-situ crop constituent data 340 was geolocated and vegetative index values from the vegetative index map 332 corresponding to the same location in the field where in-situ crop constituent data 340 was geolocated. Based on this relationship established by vegetative index-to-crop constituent model generator 342, vegetative index-to-crop constituent model generator 342 generates a predictive crop constituent model. The predictive crop constituent model is used by predictive map generator 212 to predict crop constituent values at different locations in the field based upon the georeferenced vegetative index value contained in the vegetative index map 332 at the same locations in the field.

Historical crop constituent-to-crop constituent model generator 344 identifies a relationship between the crop constituent value represented in the in-situ crop constituent data 340, at a geographic location corresponding to where the in-situ crop constituent data 340 was geolocated, and the historical crop constituent value at the same location (or a location in a historical crop constituent map 333 with similar contextual data 337 as the present area or year). The historical crop constituent value 335 is the georeferenced and contextually referenced value contained in the historical crop constituent map 333. Historical crop constituent-to-crop constituent model generator 344 then generates a predictive crop constituent model that is used by map generator 212 to predict the crop constituent value at a location in the field based upon the historical crop constituent value 335.

Soil property-to-crop constituent model generator 345 identifies a relationship between in-situ crop constituent data 340 at a geographic location corresponding to where in-situ crop constituent data 340 was geolocated and soil property values (such as soil composition values, soil type values, soil moisture values, est.) from the soil property map 343 corresponding to the same location in the field where in-situ crop constituent data 340 was geolocated. Based on this relationship established by soil property-to-crop constituent model generator 345, soil property-to-crop constituent model generator 345 generates a predictive crop constituent model. The predictive crop constituent model is used by predictive map generator 212 to predict crop constituent values at different locations in the field based upon the georeferenced soil property value contained in the soil property map 343 at the same locations in the field.

Prior operation characteristic-to-crop constituent model generator 346 identifies a relationship between in-situ crop constituent data 340 at a geographic location corresponding to where in-situ crop constituent data 340 was geolocated and prior operation characteristic values (such as substance application characteristic values) from the prior map 341 corresponding to the same location in the field where in-situ crop constituent data 340 was geolocated. Based on this relationship established by prior operation characteristic-to-crop constituent model generator 346, prior operation characteristic-to-crop constituent model generator 346 generates a predictive crop constituent model. The predictive crop constituent model is used by predictive map generator 212 to predict crop constituent values at different locations in the field based upon the georeferenced prior operation characteristic value contained in the prior operation map 341 at the same locations in the field.

Predictive characteristic-to-crop constituent model generator 357 identifies a relationship between in-situ crop constituent data 340 at a geographic location corresponding to where in-situ crop constituent data 340 was geolocated and predictive characteristic values (such as predictive biomass values, predictive yield values, or predictive crop moisture values) from the predictive map 353 corresponding to the same location in the field where in-situ crop constituent data 340 was geolocated. Based on this relationship established by predictive characteristic-to-crop constituent model generator 357, predictive characteristic-to-crop constituent model generator 357 generates a predictive crop constituent model. The predictive crop constituent model is used by predictive map generator 212 to predict crop constituent values at different locations in the field based upon the georeferenced predictive characteristic value (such as the georeferenced predictive biomass value, the georeferenced predictive yield value, or the georeferenced predictive crop moisture value) contained in the predictive characteristic map 353 at the same locations.

Agricultural characteristic-to-crop constituent model generator 348 identifies a relationship between in-situ crop constituent data 340 at a geographic location corresponding to where in-situ crop constituent data 340 was geolocated and agricultural characteristic values from the agricultural characteristic map 347 corresponding to the same location in the field where in-situ crop constituent data 340 was geolocated. Based on this relationship established by agricultural characteristic-to-crop constituent model generator 348, agricultural characteristic-to-crop constituent model generator 348 generates a predictive crop constituent model. The predictive crop constituent model is used by predictive map generator 212 to predict crop constituent values at different locations in the field based upon the georeferenced agricultural characteristic value contained in the agricultural characteristic map 347 at the same locations in the field.

In light of the above, the predictive model generator 210 is operable to produce a plurality of predictive crop constituent models, such as one or more of the predictive crop constituent models generated by model generators 342, 344, 345, 346, 348, 357, and 349. In another example, two or more of the predictive crop constituent models described above may be combined into a single predictive crop constituent model that predicts crop constituent values based upon the vegetative index value the historical crop constituent value, the soil property value, the prior operation characteristic value, the predictive characteristic value, or the agricultural characteristic value at different locations in the field or both. Any of these crop constituent models, or combinations thereof, are represented collectively by crop constituent model 350 in FIG. 4.

The predictive crop constituent model 350 is provided to predictive map generator 212. In the example of FIG. 4, predictive map generator 212 includes a crop constituent map generator 352. In other examples, the predictive map generator 212 may include additional, fewer, or different map generators. Crop constituent map generator 352 receives the predictive crop constituent model 350 that predicts crop constituent values based upon in-situ data 340 along with one or more of the vegetative index map 332, historical crop constituent map 333, prior operation map 341, soil property map 343, predictive map 353, or agricultural characteristic map 347.

Crop constituent map generator 352 can generate a functional predictive crop constituent map 360 that predicts crop constituent values at different locations in the field based upon one or more of the vegetative index value, historical crop constituent value, prior operation characteristic value, soil property value (such as a soil composition value), predictive characteristic value (such as predictive yield value, predictive biomass value, or predictive crop moisture value), or agricultural characteristic value at those locations in the field and the predictive crop constituent model 350. The crop constituent values may include an amount of one or more constituents in the crop material, such as an amount of protein, oil, starch, or other constituent in the crop material (e.g., the crop plant). In other examples, the crop constituent values may include an amount of one or more constituents in the grain of the crop plants, such as an amount of protein, oil, starch, or other constituent in the grain (e.g., corn kernel, soybean, etc.). The generated functional predictive crop constituent map 360 may be provided to control zone generator 213, control system 214, or both. Control zone generator 213 generates control zones and incorporates those control zones into the functional predictive map, i.e., predictive map 360, to produce predictive control zone map 265. One or both of functional predictive maps 264 or predictive control zone map 265 may be presented to the operator 260 or anther user or be provided to control system 214, which generates control signals to control one or more of the controllable subsystems 216 based upon the predictive map 264, predictive control zone map 265, or both.

FIG. 5 is a flow diagram of an example of operation of predictive model generator 210 and predictive map generator 212 in generating the predictive crop constituent model 350 and the functional predictive crop constituent map 360. At block 362, predictive model generator 210 and predictive map generator 212 receive one or more prior vegetative index maps 332, one or more historical crop constituent maps 333, one or more prior operation maps 341, or one or more soil composition maps 434, or one or more agricultural characteristic maps 347, or a combination thereof. At block 362, an in-situ sensor signal is received from an in-situ sensor, such as a crop constituent sensor signal from a crop constituent sensor 336.

At block 363, prior information map selector 209 selects one or more specific prior information map(s) 250 for use by predictive model generator 210. In one example, prior information map selector 209 selects a map from a plurality of candidate maps based on a comparison of the contextual information in the candidate maps with the current contextual information. For example, a candidate historical crop constituent map may be selected from a prior year in which weather conditions over the growth season were similar to the present year's weather conditions. Or, for example, a candidate historical crop constituent map may be selected from a prior year having a below average level of precipitation, while the present year has an average or above average level of precipitation, because the historical crop constituent map associated with a previous year with below average precipitation may still have a useful historical crop constituent-to-crop constituent relationship, as discussed above. In some examples, prior information map selector 209 can change which prior information map is being used upon detection that one of the other candidate prior information maps is more closely correlating to the in-situ sensed crop constituent.

At block 372, processing system 338 processes the one or more received sensor signals received from the in-situ sensors 208, such as the one or more received sensor signals from crop constituent sensors 336 to generate a crop constituent value indicative of an amount of one or more constituents present within harvested crop material, such as an amount of protein, an amount of starch, or an amount of oil, as well as an amount of various other crop constituents. Other in-situ sensors 208 can also be used, as indicated by block 370. Some examples of other in-situ sensors are shown in FIG. 6B.

At block 382, predictive model generator 210 also obtains the geographic location corresponding to the sensor signal. For instance, the predictive model generator 210 can obtain the geographic position from geographic position sensor 204 and determine, based upon machine delays (e.g., machine processing speed) and machine speed, an accurate geographic location where the in-situ sensed crop constituent value is to be attributed. For example, the location of the agricultural harvester at the exact time a crop constituent sensor signal is captured may not correspond to the geographic location of the crop constituent value. Thus, a position of the agricultural harvester 100 when the crop constituent sensor signal is obtained may not correspond to the location where the crop, having the crop constituent, was planted.

At block 384, predictive model generator 210 generates one or more predictive crop constituent models, such as crop constituent model 350, that model a relationship between at least one of a vegetative index value, historical crop constituent value, prior operation characteristic value, soil property value, a predictive characteristic value, or agricultural characteristic value obtained from a map, and a crop constituent value being detected by the in-situ sensor 208. For instance, predictive model generator 210 may generate a predictive crop constituent model based on a vegetative index value, a historical crop constituent value, a prior operation characteristic value, a soil property value, a predictive characteristic value, or a agricultural characteristic value and a detected crop constituent value indicated by the sensor signal obtained from in-situ sensor 208.

At block 386, the predictive crop constituent model, such as predictive crop constituent model 350, is provided to predictive map generator 212 which generates a functional predictive crop constituent map that maps a predicted crop constituent value to different geographic locations in the field based on the vegetative index map 332, the historical crop constituent map 333, the prior operation map 341, the soil property map 343, the predictive map 353, or the agricultural characteristic map 347, and the predictive crop constituent model 350. For instance, in some examples, the functional predictive crop constituent map 360 predicts crop constituent values. In other examples, the functional predictive crop constituent map 360 map predicts other items. Further, the functional predictive crop constituent map 360 can be generated during the course of an agricultural harvesting operation. Thus, as an agricultural harvester is moving through a field performing an agricultural harvesting operation, the functional predictive crop constituent map 360 is generated.

At block 394, predictive map generator 212 outputs the functional predictive crop constituent map 360. At block 393, predictive map generator 212 configures the functional predictive crop constituent map 360 for consumption by control system 214. At block 395, predictive map generator 212 can also provide the map 360 to control zone generator 213 for generation of and incorporation of control zones. At block 397, predictive map generator 212 configures the map 360 in other ways as well. The functional predictive crop constituent map 360 (with or without the control zones) is provided to control system 214. At block 396, control system 214 generates control signals to control the controllable subsystems 216 based upon the functional predictive crop constituent map 360.

Control system 214 can generate control signals to control header or other machine actuator(s) 248, such as to control a position or spacing of the deck plates. Control system 214 can generate control signals to control propulsion subsystem 250. Control system 214 can generate control signals to control steering subsystem 252. Control system 214 can generate control signals to control residue subsystem 138. Control system 214 can generate control signals to control machine cleaning subsystem 254. Control system 214 can generate control signals to control thresher 110. Control system 214 can generate control signals to control material handling subsystem 125. Control system 214 can generate control signals to control crop cleaning subsystem 118. Control system 214 can generate control signals to control communication system 206. Control system 214 can generate control signals to control operator interface mechanisms 218. Control system 214 can generate control signals to control various other controllable subsystems 256.

In an example in which control system 214 receives a functional predictive map or a functional predictive map with control zones added, header/reel controller 238 controls header or other machine actuators 248 to control a height, tilt, or roll of header 102. In an example in which control system 214 receives a functional predictive map or a functional predictive map with control zones added, feed rate controller 236 controls propulsion subsystem 250 to control a travel speed of agricultural harvester 100. In an example in which control system 214 receives a functional predictive map or a functional predictive map with control zones added, the path planning controller 234 controls steering subsystem 252 to steer agricultural harvester 100. In another example in which control system 214 receives afunctional predictive map or a functional predictive map with control zones added, the residue system controller 244 controls residue subsystem 138. In another example in which control system 214 receives a functional predictive map or a functional predictive map with control zones added, the settings controller 232 controls thresher settings of thresher 110. In another example in which control system 214 receives a functional predictive map or a functional predictive map with control zones added, the settings controller 232 or another controller 246 controls material handling subsystem 125. In another example in which control system 214 receives a functional predictive map or a functional predictive map with control zones added, the settings controller 232 controls crop cleaning subsystem 118. In another example in which control system 214 receives a functional predictive map or a functional predictive map with control zones added, the machine cleaning controller 245 controls machine cleaning subsystem 254 on agricultural harvester 100. In another example in which control system 214 receives a functional predictive map or a functional predictive map with control zones added, the communication system controller 229 controls communication system 206. In another example in which control system 214 receives a functional predictive map or a functional predictive map with control zones added, the operator interface controller 231 controls operator interface mechanisms 218 on agricultural harvester 100. In another example in which control system 214 receives the functional predictive map or the functional predictive map with control zones added, the deck plate position controller 242 controls machine/header actuators 248 to control a deck plate on agricultural harvester 100. In another example in which control system 214 receives the functional predictive map or the functional predictive map with control zones added, the draper belt controller 240 controls machine/header actuators 248 to control a draper belt on agricultural harvester 100. In another example in which control system 214 receives the functional predictive map or the functional predictive map with control zones added, the other controllers 246 control other controllable subsystems 256 on agricultural harvester 100.

It can thus be seen that the present system takes a map that maps a characteristic such as a vegetative index value, a historical crop constituent value, a prior operation characteristic value, a soil property value, a predictive characteristic value, or an agricultural characteristic value to different locations in a field. The present system also uses one or more in-situ sensors that sense in-situ sensor data that is indicative of a characteristic, such as crop constituent value, and generates a model that models a relationship between the crop constituent value detected in-situ using the in-situ sensor and the characteristic value mapped in the map. Thus, the present system generates a functional predictive map using a model and a map and may configure the generated functional predictive map for consumption by a control system or for presentation to a local or remote operator or other user. For example, the control system may use the map to control one or more systems of a combine harvester.

FIG. 6A is a block diagram of an example portion of the agricultural harvester 100 shown in FIG. 1. Particularly, FIG. 6A shows, among other things, examples of predictive model generator 210 and predictive map generator 212. In the illustrated example, the prior information map 258 is a historical crop constituent map 333. Historical crop constituent map 333 may include historical crop constituent values at various locations in the field from a prior operation on the field, such as a previous harvesting operation on the field. FIG. 6A also shows that predictive model generator 210 and predictive map generator can receive, alternatively or in addition to prior information map 258, a predictive crop constituent map, such as functional predictive crop constituent map 360. Functional predictive crop constituent map 360 can be used similarly as prior information map 258 in that model generator 210 models a relationship between information provided by functional predictive crop constituent map 360 and characteristics sensed by in-situ sensors 208, and map generator 212 can thus use the model to generate a functional predictive map that predicts the characteristics sensed by the in-situ sensors 208, or a characteristic indicative of the sensed characteristic, at different locations in the field based upon one or more of the values in the functional predictive crop constituent map 360 at those locations in the field and based on the predictive model. As illustrated in FIG. 6A, predictive model generator 210 and predictive map generator 212 can also receive other maps 401, for instance other prior information maps or other predictive maps, such as other predictive crop constituent maps generated in other ways than functional predictive crop constituent map 360.

Also, in the example shown in FIG. 6A, in-situ sensor 208 can include one or more of an agricultural characteristic sensor 402, operator input sensor 404, and a processing system 406. In-situ sensors 208 can include other sensors 408 as well.

Agricultural characteristic sensor 402 senses values indicative of agricultural characteristics. Operator input sensor 404 senses various operator inputs. The inputs can be setting inputs for controlling the settings on agricultural harvester 100 or other control inputs, such as steering inputs and other inputs. Thus, when operator 260 changes a setting or provides a commanded input through an operator interface mechanism 218, such an input is detected by operator input sensor 404, which provides a sensor signal indicative of that sensed operator input.

Processing system 406 may receive the sensor signals from one or more of agricultural characteristic sensor 402, and operator input sensor 404 and generate an output indicative of the sensed characteristic. For instance, processing system 406 may receive a sensor input from agricultural characteristic sensor 402 and generate an output indicative of an agricultural characteristic. Processing system 406 may also receive an input from operator input sensor 404 and generate an output indicative of the sensed operator input.

Predictive model generator 210 may include crop constituent-to-agricultural characteristic model generator 410 and crop constituent-to-command model generator 414. In other examples, predictive model generator 210 can include additional, fewer, or other model generators 415. For example, predictive model generator 210 may include specific agricultural characteristics or specific operator command model generators, such as model generators using specific agricultural characteristics, for instance, biomass or biomass characteristics, yield or yield characteristics, as well as various other agricultural characteristics, or model generators using specific operator commands, for instance, header height settings, steering settings (such as travel direction), speed settings (such as travel speed), threshing rotor settings, as well as various other machine settings. Predictive model generator 210 may receive a geographic location indicator 334 from geographic position sensor 204 and generate a predictive model 426 that models a relationship between the information in one or more of the prior information maps 258 or the information in functional predictive crop constituent map 360 and one or more of: the agricultural characteristic sensed by agricultural characteristic sensor 402; and operator input commands sensed by operator input sensor 404.

Crop constituent-to-agricultural characteristic model generator 410 generates a model that models a relationship between crop constituent values (which may be on predictive crop constituent map 360, historical crop constituent map 333, or other map 401) and the agricultural characteristic sensed by agricultural characteristic sensor 402. Crop constituent-to-agricultural characteristic model generator 410 generates a predictive model 426 that corresponds to this relationship.

Crop constituent-to-operator command model generator 414 generates a model that models a relationship between crop constituent values as reflected on predictive crop constituent map 360, historical crop constituent map 333, or other map 401 and operator input commands that are sensed by operator input sensor 404. Crop constituent-to-operator command model generator 414 generates a predictive model 426 that corresponds to this relationship.

Other model generators 415 may include, for example, specific agricultural characteristics or specific operator command model generators, such as model generators using specific agricultural characteristics, for instance, biomass or biomass characteristics, yield or yield characteristics, as well as various other agricultural characteristics, or model generators using specific operator commands, for instance, header height settings, steering settings, speed settings, threshing rotor settings, as well as various other machine settings

Predictive model 426 generated by the predictive model generator 210 can include one or more of the predictive models that may be generated by crop constituent-to-agricultural characteristic model generator 410 and crop constituent-to-operator command model generator 414, and other model generators that may be included as part of other items 415.

In the example of FIG. 6A, predictive map generator 212 includes predictive agricultural characteristic map generator 416 and a predictive operator command map generator 422. In other examples, predictive map generator 212 can include additional, fewer, or other map generators 424.

Predictive agricultural characteristic map generator 416 receives a predictive model 426 that models the relationship between a crop constituent value and an agricultural characteristic sensed by agricultural characteristic 402 (such as a predictive model generated by crop constituent-to-agricultural characteristic model generator 410), and one or more of the prior information maps 258 or functional predictive crop constituent map 360, or other maps 401. Predictive agricultural characteristic map generator 416 generates a functional predictive agricultural characteristic map 427 that predicts agricultural characteristic values (or the agricultural characteristics the values are indicative of) at different locations in the field based upon one or more of the crop constituent values in one or more of the prior information maps 258 or the functional predictive crop constituent map 360, or other map 401 at those locations in the field and based on predictive model 426.

Predictive operator command map generator 422 receives a predictive model 426 that models the relationship between a crop constituent value and operator command inputs detected by operator input sensor 404 (such as a predictive model generated by crop constituent-to-command model generator 414), and one or more of the prior information maps 258 or the functional predictive crop constituent map 360, or other map 401. Predictive operator command map generator 422 generates a functional predictive operator command map 440 that predicts operator command inputs at different locations in the field based upon the crop constituent values from the prior information map 258 or the functional predictive crop constituent map 360, or other map 401, at those locations in the field and based on predictive model 426.

Predictive map generator 212 outputs one or more of the functional predictive maps 427 and 440. Each of the functional predictive maps 427 and 440 may be provided to control zone generator 213, control system 214, or both. Control zone generator 213 generates and incorporates control zones to provide a functional predictive map 427 with control zones and a functional predictive map 440 with control zones. Any or all of functional predictive maps 427 and 440 (with or without control zones) may be provided to control system 214, which generates control signals to control one or more of the controllable subsystems 216 based upon one or all of the functional predictive maps 427 and 440 (with or without control zones). Any or all of the maps 427 and 440 (with or without control zones) may be presented to operator 260 or another user.

FIG. 6B is a block diagram showing some examples of real-time (in-situ) sensors 208. Some of the sensors shown in FIG. 6B, or different combinations of them, may have both a sensor 402 and a processing system 406, while others may act as sensor 402 described with respect to FIGS. 6A and 7 where the processing system 406 is separate. Some of the possible in-situ sensors 208 shown in FIG. 6B are shown and described above with respect to previous FIGS. and are similarly numbered. FIG. 6B shows that in-situ sensors 208 can include operator input sensors 980, machine sensors 982, harvested material property sensors 984, field and soil property sensors 985, environmental characteristic sensors 987, and they may include a wide variety of other sensors 226. Operator input sensors 980 may be sensors that sense operator inputs through operator interface mechanisms 218. Therefore, operator input sensors 980 may sense user movement of linkages, joysticks, a steering wheel, buttons, dials, or pedals. Operator input sensors 980 can also sense user interactions with other operator input mechanisms, such as with a touch sensitive screen, with a microphone where speech recognition is utilized, or any of a wide variety of other operator input mechanisms.

Machine sensors 982 may sense different characteristics of agricultural harvester 100. For instance, as discussed above, machine sensors 982 may include machine speed sensors 146, separator loss sensor 148, clean grain camera 150, forward looking image capture mechanism 151, loss sensors 152 or geographic position sensor 204, examples of which are described above. Machine sensors 982 can also include machine setting sensors 991 that sense machine settings. Some examples of machine settings were described above with respect to FIG. 1. Front-end equipment (e.g., header) position sensor 993 can sense the position of the header 102, reel 164, cutter 104, or other front-end equipment relative to the frame of agricultural harvester 100 or relative to the ground. For instance, sensors 993 may sense the height of header 102 above the ground. Machine sensors 982 can also include front-end equipment (e.g., header) orientation sensors 995. Sensors 995 may sense the orientation of header 102 relative to agricultural harvester 100, or relative to the ground. Machine sensors 982 may include stability sensors 497. Stability sensors 997 sense oscillation or bouncing motion (and amplitude) of agricultural harvester 100. Machine sensors 982 may also include residue setting sensors 999 that are configured to sense whether agricultural harvester 100 is configured to chop the residue, produce a windrow, or deal with the residue in another way. Machine sensors 982 may include cleaning shoe fan speed sensor 951 that senses the speed of cleaning fan 120. Machine sensors 982 may include concave clearance sensors 953 that sense the clearance between the rotor 112 and concaves 114 on agricultural harvester 100. Machine sensors 982 may include chaffer clearance sensors 955 that sense the size of openings in chaffer 122. The machine sensors 982 may include threshing rotor speed sensor 957 that senses a rotor speed of rotor 112. Machine sensors 982 may include rotor force sensor 959 that senses the force used to drive rotor 112. Machine sensors 982 may include sieve clearance sensor 961 that senses the size of openings in sieve 124. The machine sensors 982 may include MOG moisture sensor 963 that senses a moisture level of the MOG passing through agricultural harvester 100. Machine sensors 982 may include machine orientation sensor 965 that senses the orientation of agricultural harvester 100. Machine sensors 982 may include material feed rate sensors 967 that sense the feed rate of material as the material travels through feeder house 106, clean grain elevator 130, or elsewhere in agricultural harvester 100. Machine sensors 982 can include biomass sensors 969 that sense the biomass traveling through feeder house 106, through separator 116, or elsewhere in agricultural harvester 100. The machine sensors 982 may include fuel consumption sensor 971 that senses a rate of fuel consumption over time of agricultural harvester 100. Machine sensors 982 may include power utilization sensor 973 that senses power utilization in agricultural harvester 100, such as which subsystems are utilizing power, or the rate at which subsystems are utilizing power, or the distribution of power among the subsystems in agricultural harvester 100. Machine sensors 982 may include tire pressure sensors 977 that sense the inflation pressure in tires 144 of agricultural harvester 100. Machine sensor 982 may include a wide variety of other machine performance sensors, or machine characteristic sensors, indicated by block 975. The machine performance sensors and machine characteristic sensors 975 may sense machine performance or characteristics of agricultural harvester 100.

Harvested material property sensors 984 may sense characteristics of the severed crop material as the crop material is being processed by agricultural harvester 100. The crop properties may include such things as crop type, crop constituents such as starches, oil, and protein, crop moisture, grain quality (such as broken grain), MOG levels, grain constituents such as starches, oil, and protein, MOG moisture, and other crop material properties. Other sensors could sense straw “toughness”, adhesion of corn to ears, and other characteristics that might be beneficially used to control processing for better grain capture, reduced grain damage, reduced power consumption, reduced grain loss, etc.

Field and soil property sensors 985 may sense characteristics of the field and soil. The field and soil properties may include soil moisture, soil compactness, the presence and location of standing water, soil type, and other soil and field characteristics.

Environmental characteristic sensors 987 may sense one or more environmental characteristics. The environmental characteristics may include such things as wind direction and wind speed, precipitation, fog, dust level or other obscurants, or other environmental characteristics.

FIG. 7 shows a flow diagram illustrating one example of the operation of predictive model generator 210 and predictive map generator 212 in generating one or more predictive models 426 and one or more functional predictive maps 427 and 440. At block 442, predictive model generator 210 and predictive map generator 212 receive a map. The map received by predictive model generator 210 or predictive map generator in generating one or more predictive models 426 and one or more functional predictive maps 427 and 440 may be prior information map 258, such as a historical crop constituent map 333 created using data obtained during a prior operation in a field, such as a prior harvesting operation. The map received by predictive model generator 210 or predictive map generator in generating one or more predictive models 426 and one or more functional predictive maps 427 and 440 may be functional predictive crop constituent map 360. Other maps can be received as well as indicated by block 401, such as other prior information maps or other predictive maps, for instance, other predictive crop constituent maps generated in ways other than functional predictive crop constituent map 360.

At block 444, predictive model generator 210 receives a sensor signal containing sensor data from an in-situ sensor 208. The in-situ sensor can be one or more of an agricultural characteristic sensor 402 and an operator input sensor 404. Agricultural characteristic sensor 402 senses an agricultural characteristic. Operator input sensor 404 senses an operator input command. Predictive model generator 210 can receive other in-situ sensor inputs as well, as indicated by block 408.

At block 454, processing system 406 processes the data contained in the sensor signal or signals received from the in-situ sensor or sensors 208 to obtain processed data 409, shown in FIG. 6A. The data contained in the sensor signal or signals can be in a raw format that is processed to receive processed data 409. For example, a temperature sensor signal includes electrical resistance data, this electrical resistance data can be processed into temperature data. In other examples, processing may comprise digitizing, encoding, formatting, scaling, filtering, or classifying data. The processed data 409 may be indicative of one or more of an agricultural characteristic or an operator input command. The processed data 409 is provided to predictive model generator 210.

Returning to FIG. 7, at block 456, predictive model generator 210 also receives a geographic location 334, or an indication of a geographic location, from geographic position sensor 204, as shown in FIG. 6A. The geographic location 334 may be correlated to the geographic location from which the sensed variable or variables, sensed by in-situ sensors 208, were taken. For instance, the predictive model generator 210 can obtain the geographic location 334 from geographic position sensor 204 and determine, based upon machine delays, machine speed, etc., a precise geographic location from which the processed data 409 was derived.

At block 458, predictive model generator 210 generates one or more predictive models 426 that model a relationship between a mapped value in a received map and a characteristic represented in the processed data 409. For example, in some instances, the mapped value in a received map may be a crop constituent value, such as an amount of protein, starch, oil, or other constituent in the crop plant or an amount of protein, starch, oil, or other constituent in grain of the crop plant, and the predictive model generator 210 generates a predictive model using the mapped value of a received map and a characteristic sensed by in-situ sensors 208, as represented in the processed data 409, or a related characteristic, such as a characteristic that correlates to the characteristic sensed by in-situ sensors 208.

The one or more predictive models 426 are provided to predictive map generator 212. At block 466, predictive map generator 212 generates one or more functional predictive maps. The functional predictive maps may be functional predictive agricultural characteristic map 427 and a functional predictive operator command map 440, or any combination of these maps. Functional predictive agricultural characteristic map 427 predicts agricultural characteristic values (or agricultural characteristics indicated by the values) at different locations in the field. Functional predictive operator command map 440 predicts desired or likely operator command inputs at different locations in the field. Further, one or more of the functional predictive maps 427 and 440 can be generated during the course of an agricultural operation. Thus, as agricultural harvester 100 is moving through a field performing an agricultural operation, the one or more predictive maps 427 and 440 are generated as the agricultural operation is being performed.

At block 468, predictive map generator 212 outputs the one or more functional predictive maps 427 and 440. At block 470, predictive map generator 212 may configure the map for presentation to and possible interaction by an operator 260 or another user. At block 472, predictive map generator 212 may configure the map for consumption by control system 214. At block 474, predictive map generator 212 can provide the one or more predictive maps 427 and 440 to control zone generator 213 for generation of control zones. At block 476, predictive map generator 212 configures the one or predictive maps 427 and 440 in other ways. In an example in which the one or more functional predictive maps 427 and 440 are provided to control zone generator 213, the one or more functional predictive maps 427 and 440, with the control zones included therewith, represented by corresponding maps 265, described above, may be presented to operator 260 or another user or provided to control system 214 as well.

At block 478, control system 214 then generates control signals to control the controllable subsystems based upon the one or more functional predictive maps 427 and 440 (or the functional predictive maps 427 and 440 having control zones) as well as an input from the geographic position sensor 204.

Control system 214 can generate control signals to control header or other machine actuator(s) 248, such as to control a position or spacing of the deck plates. Control system 214 can generate control signals to control propulsion subsystem 250. Control system 214 can generate control signals to control steering subsystem 252. Control system 214 can generate control signals to control residue subsystem 138. Control system 214 can generate control signals to control machine cleaning subsystem 254. Control system 214 can generate control signals to control thresher 110. Control system 214 can generate control signals to control material handling subsystem 125. Control system 214 can generate control signals to control crop cleaning subsystem 118. Control system 214 can generate control signals to control communication system 206. Control system 214 can generate control signals to control operator interface mechanisms 218. Control system 214 can generate control signals to control various other controllable subsystems 256.

In an example in which control system 214 receives a functional predictive map or a functional predictive map with control zones added, header/reel controller 238 controls header or other machine actuators 248 to control a height, tilt, or roll of header 102. In an example in which control system 214 receives a functional predictive map or a functional predictive map with control zones added, feed rate controller 236 controls propulsion subsystem 250 to control a travel speed of agricultural harvester 100. In an example in which control system 214 receives a functional predictive map or a functional predictive map with control zones added, the path planning controller 234 controls steering subsystem 252 to steer agricultural harvester 100. In another example in which control system 214 receives a functional predictive map or a functional predictive map with control zones added, the residue system controller 244 controls residue subsystem 138. In another example in which control system 214 receives a functional predictive map or a functional predictive map with control zones added, the settings controller 232 controls thresher settings of thresher 110. In another example in which control system 214 receives a functional predictive map or a functional predictive map with control zones added, the settings controller 232 or another controller 246 controls material handling subsystem 125. In another example in which control system 214 receives a functional predictive map or a functional predictive map with control zones added, the settings controller 232 controls crop cleaning subsystem 118. In another example in which control system 214 receives a functional predictive map or a functional predictive map with control zones added, the machine cleaning controller 245 controls machine cleaning subsystem 254 on agricultural harvester 100. In another example in which control system 214 receives a functional predictive map or a functional predictive map with control zones added, the communication system controller 229 controls communication system 206. In another example in which control system 214 receives a functional predictive map or a functional predictive map with control zones added, the operator interface controller 231 controls operator interface mechanisms 218 on agricultural harvester 100. In another example in which control system 214 receives the functional predictive map or the functional predictive map with control zones added, the deck plate position controller 242 controls machine/header actuators 248 to control a deck plate on agricultural harvester 100. In another example in which control system 214 receives the functional predictive map or the functional predictive map with control zones added, the draper belt controller 240 controls machine/header actuators 248 to control a draper belt on agricultural harvester 100. In another example in which control system 214 receives the functional predictive map or the functional predictive map with control zones added, the other controllers 246 control other controllable subsystems 256 on agricultural harvester 100.

FIG. 8 shows a block diagram illustrating one example of control zone generator 213. Control zone generator 213 includes work machine actuator (WMA) selector 486, control zone generation system 488, and regime zone generation system 490. Control zone generator 213 may also include other items 492. Control zone generation system 488 includes control zone criteria identifier component 494, control zone boundary definition component 496, target setting identifier component 498, and other items 520. Regime zone generation system 490 includes regime zone criteria identification component 522, regime zone boundary definition component 524, settings resolver identifier component 526, and other items 528. Before describing the overall operation of control zone generator 213 in more detail, a brief description of some of the items in control zone generator 213 and the respective operations thereof will first be provided.

Agricultural harvester 100, or other work machines, may have a wide variety of different types of controllable actuators that perform different functions. The controllable actuators on agricultural harvester 100 or other work machines are collectively referred to as work machine actuators (WMAs). Each WMA may be independently controllable based upon values on a functional predictive map, or the WMAs may be controlled as sets based upon one or more values on a functional predictive map. Therefore, control zone generator 213 may generate control zones corresponding to each individually controllable WMA or corresponding to the sets of WMAs that are controlled in coordination with one another.

WMA selector 486 selects a WMA or a set of WMAs for which corresponding control zones are to be generated. Control zone generation system 488 then generates the control zones for the selected WMA or set of WMAs. For each WMA or set of WMAs, different criteria may be used in identifying control zones. For example, for one WMA, the WMA response time may be used as the criteria for defining the boundaries of the control zones. In another example, wear characteristics (e.g., how much a particular actuator or mechanism wears as a result of movement thereof) may be used as the criteria for identifying the boundaries of control zones. Control zone criteria identifier component 494 identifies particular criteria that are to be used in defining control zones for the selected WMA or set of WMAs. Control zone boundary definition component 496 processes the values on a functional predictive map under analysis to define the boundaries of the control zones on that functional predictive map based upon the values in the functional predictive map under analysis and based upon the control zone criteria for the selected WMA or set of WMAs.

Target setting identifier component 498 sets a value of the target setting that will be used to control the WMA or set of WMAs in different control zones. For instance, if the selected WMA is crop cleaning subsystem 118 and the functional predictive map under analysis is a functional predictive crop constituent map 360, then the target setting in each control zone may be a target speed setting for cleaning fan 120 based on crop constituent values contained in the functional predictive crop constituent map 360.

In some examples, where agricultural harvester 100 is to be controlled based on a current or future location of the agricultural harvester 100, multiple target settings may be possible for a WMA at a given position. In that case, the target settings may have different values and may be competing. Thus, the target settings need to be resolved so that only a single target setting is used to control the WMA. For example, where the WMA is an actuator in propulsion system 250 that is being controlled in order to control the speed of agricultural harvester 100, multiple different competing sets of criteria may exist that are considered by control zone generation system 488 in identifying the control zones and the target settings for the selected WMA in the control zones. For instance, different target settings for controlling the speed of the cleaning fan may be generated based upon, for example, a detected or predicted crop constituent value, a historical crop constituent value, a detected or predicted agricultural characteristic value, a detected or predicted vegetative index value, predicted characteristic values, such as predicted biomass, predicted yield, or predicted crop moisture, a detected or predicted soil property value (such as an amount of nitrogen in the soil), a detected or predicted prior operating characteristic value (such as a substance application characteristic value, for instance a fertilizer application characteristic value), a detected or predicted feed rate value, a detected or predictive fuel efficiency value, a detected or predicted grain loss value, or a combination of these. It will be noted that these are merely example and target settings for various WMAs can be based on various other values or combinations of values. However, at any given time, the cleaning fan 120 cannot operate at multiple speeds simultaneously. Rather, at any given time, the cleaning fan 120 operates at a single speed. Thus, one of the competing target settings is selected to control the speed of cleaning fan 120.

Therefore, in some examples, regime zone generation system 490 generates regime zones to resolve multiple different competing target settings. Regime zone criteria identification component 522 identifies the criteria that are used to establish regime zones for the selected WMA or set of WMAs on the functional predictive map under analysis. Some criteria that can be used to identify or define regime zones include, for example, crop constituent values, agricultural characteristic values, prior operation characteristic values, predicted characteristic values, vegetative index values, soil property values, operator command inputs, crop type or crop variety (for example based on an as-planted map or another source of the crop type or crop variety), weed type, weed intensity, or crop state (such as whether the crop is down, partially down or standing). These are merely some examples of the criteria that can be used to identify or define regime zones. Just as each WMA or set of WMAs may have a corresponding control zone, different WMAs or sets of WMAs may have a corresponding regime zone. Regime zone boundary definition component 524 identifies the boundaries of regime zones on the functional predictive map under analysis based on the regime zone criteria identified by regime zone criteria identification component 522.

In some examples, regime zones may overlap with one another. For instance, a crop variety regime zone may overlap with a portion of or an entirety of a crop state regime zone. In such an example, the different regime zones may be assigned to a precedence hierarchy so that, where two or more regime zones overlap, the regime zone assigned with a greater hierarchical position or importance in the precedence hierarchy has precedence over the regime zones that have lesser hierarchical positions or importance in the precedence hierarchy. The precedence hierarchy of the regime zones may be manually set or may be automatically set using a rules-based system, a model-based system, or another system. As one example, where a downed crop regime zone overlaps with a crop variety regime zone, the downed crop regime zone may be assigned a greater importance in the precedence hierarchy than the crop variety regime zone so that the downed crop regime zone takes precedence.

In addition, each regime zone may have a unique settings resolver for a given WMA or set of WMAs. Settings resolver identifier component 526 identifies a particular settings resolver for each regime zone identified on the functional predictive map under analysis and a particular settings resolver for the selected WMA or set of WMAs.

Once the settings resolver for a particular regime zone is identified, that settings resolver may be used to resolve competing target settings, where more than one target setting is identified based upon the control zones. The different types of settings resolvers can have different forms. For instance, the settings resolvers that are identified for each regime zone may include a human choice resolver in which the competing target settings are presented to an operator or other user for resolution. In another example, the settings resolver may include a neural network or other artificial intelligence or machine learning system. In such instances, the settings resolvers may resolve the competing target settings based upon a predicted or historical quality metric corresponding to each of the different target settings. As an example, an increased cleaning fan may reduce loss but improve grain quality. A reduced cleaning fan speed setting may reduce grain loss but may improve grain quality. When grain loss is selected as a quality metric, the predicted or historical value for the selected quality metric, given the two competing vehicle speed settings values, may be used to resolve the cleaning fan speed setting. In some instances, the settings resolvers may be a set of threshold rules that may be used instead of, or in addition to, the regime zones. An example of a threshold rule may be expressed as follows:

-   -   If predicted crop constituent values within 20 feet of the         header of the agricultural harvester 100 are greater than x         (where x is a selected or predetermined value), then use the         target setting value that is chosen based on grain loss over         other competing target settings, otherwise use the target         setting value based on grain quality over other competing target         setting values.

The settings resolvers may be logical components that execute logical rules in identifying a target setting. For instance, the settings resolver may resolve target settings while attempting to minimize harvest time or minimize the total harvest cost or maximize harvested grain or based on other variables that are computed as a function of the different candidate target settings. A harvest time may be minimized when an amount to complete a harvest is reduced to at or below a selected threshold. A total harvest cost may be minimized where the total harvest cost is reduced to at or below a selected threshold. Harvested grain may be maximized where the amount of harvested grain is increased to at or above a selected threshold.

FIG. 9 is a flow diagram illustrating one example of the operation of control zone generator 213 in generating control zones and regime zones for a map that the control zone generator 213 receives for zone processing (e.g., for a map under analysis).

At block 530, control zone generator 213 receives a map under analysis for processing. In one example, as shown at block 532, the map under analysis is a functional predictive map. For example, the map under analysis may be one of the functional predictive maps 360, 427 or 440. Block 534 indicates that the map under analysis can be other maps as well.

At block 536, WMA selector 486 selects a WMA or a set of WMAs for which control zones are to be generated on the map under analysis. At block 538, control zone criteria identification component 494 obtains control zone definition criteria for the selected WMAs or set of WMAs. Block 540 indicates an example in which the control zone criteria are or include wear characteristics of the selected WMA or set of WMAs. Block 542 indicates an example in which the control zone definition criteria are or include a magnitude and variation of input source data, such as the magnitude and variation of the values on the map under analysis or the magnitude and variation of inputs from various in-situ sensors 208. Block 544 indicates an example in which the control zone definition criteria are or include physical machine characteristics, such as the physical dimensions of the machine, a speed at which different subsystems operate, or other physical machine characteristics. Block 546 indicates an example in which the control zone definition criteria are or include a responsiveness of the selected WMA or set of WMAs in reaching newly commanded setting values. Block 548 indicates an example in which the control zone definition criteria are or include machine performance metrics. Block 550 indicates an example in which the control zone definition criteria are or includes operator preferences. Block 552 indicates an example in which the control zone definition criteria are or include other items as well. Block 549 indicates an example in which the control zone definition criteria are time based, meaning that agricultural harvester 100 will not cross the boundary of a control zone until a selected amount of time has elapsed since agricultural harvester 100 entered a particular control zone. In some instances, the selected amount of time may be a minimum amount of time. Thus, in some instances, the control zone definition criteria may prevent the agricultural harvester 100 from crossing a boundary of a control zone until at least the selected amount of time has elapsed. Block 551 indicates an example in which the control zone definition criteria are based on a selected size value. For example, control zone definition criteria that are based on a selected size value may preclude definition of a control zone that is smaller than the selected size. In some instances, the selected size may be a minimum size.

At block 554, regime zone criteria identification component 522 obtains regime zone definition criteria for the selected WMA or set of WMAs. Block 556 indicates an example in which the regime zone definition criteria are based on a manual input from operator 260 or another user. Block 558 illustrates an example in which the regime zone definition criteria are based on crop constituent values, including detected, predicted, or historical crop constituent values. Block 559 illustrates an example in which the regime zone definition criteria are based on vegetative index values. Block 560 illustrates an example in which the regime zone definition criteria are based on prior operation characteristics values. Block 561 illustrates an example in which the regime zone definition criteria are based on soil property values. Block 564 indicates an example in which the regime zone definition criteria are or include other criteria as well, for example various agricultural characteristic values, such as a number of predicted characteristic values, such as predicted yield values, predicted biomass values, or predicted crop moisture values.

At block 566, control zone boundary definition component 496 generates the boundaries of control zones on the map under analysis based upon the control zone criteria. Regime zone boundary definition component 524 generates the boundaries of regime zones on the map under analysis based upon the regime zone criteria. Block 568 indicates an example in which the zone boundaries are identified for the control zones and the regime zones. Block 570 shows that target setting identifier component 498 identifies the target settings for each of the control zones. The control zones and regime zones can be generated in other ways as well, and this is indicated by block 572.

At block 574, settings resolver identifier component 526 identifies the settings resolver for the selected WMAs in each regime zone defined by regimes zone boundary definition component 524. As discussed above, the regime zone resolver can be a human resolver 576, an artificial intelligence or machine learning system resolver 578, a resolver 580 based on predicted or historical quality for each competing target setting, a rules-based resolver 582, a performance criteria-based resolver 584, or other resolvers 586.

At block 588, WMA selector 486 determines whether there are more WMAs or sets of WMAs to process. If additional WMAs or sets of WMAs are remaining to be processed, processing reverts to block 436 where the next WMA or set of WMAs for which control zones and regime zones are to be defined is selected. When no additional WMAs or sets of WMAs for which control zones or regime zones are to be generated are remaining, processing moves to block 590 where control zone generator 213 outputs a map with control zones, target settings, regime zones, and settings resolvers for each of the WMAs or sets of WMAs. As discussed above, the outputted map can be presented to operator 260 or another user; the outputted map can be provided to control system 214; or the outputted map can be output in other ways.

FIG. 10 illustrates one example of the operation of control system 214 in controlling agricultural harvester 100 based upon a map that is output by control zone generator 213. Thus, at block 592, control system 214 receives a map of the worksite. In some instances, the map can be a functional predictive map that may include control zones and regime zones, as represented by block 594. In some instances, the received map may be a functional predictive map that excludes control zones and regime zones. Block 596 indicates an example in which the received map of the worksite can be a prior information map having control zones and regime zones identified on it. Block 598 indicates an example in which the received map can include multiple different maps or multiple different map layers. Block 610 indicates an example in which the received map can take other forms as well.

At block 612, control system 214 receives a sensor signal from geographic position sensor 204. The sensor signal from geographic position sensor 204 can include data that indicates the geographic location 614 of agricultural harvester 100, the speed 616 of agricultural harvester 100, the heading 618 or agricultural harvester 100, or other information 620. At block 622, zone controller 247 selects a regime zone, and, at block 624, zone controller 247 selects a control zone on the map based on the geographic position sensor signal. At block 626, zone controller 247 selects a WMA or a set of WMAs to be controlled. At block 628, zone controller 247 obtains one or more target settings for the selected WMA or set of WMAs. The target settings that are obtained for the selected WMA or set of WMAs may come from a variety of different sources. For instance, block 630 shows an example in which one or more of the target settings for the selected WMA or set of WMAs is based on an input from the control zones on the map of the worksite. Block 632 shows an example in which one or more of the target settings is obtained from human inputs from operator 260 or another user. Block 634 shows an example in which the target settings are obtained from an in-situ sensor 208. Block 636 shows an example in which the one or more target settings is obtained from one or more sensors on other machines working in the same field either concurrently with agricultural harvester 100 or from one or more sensors on machines that worked in the same field in the past. Block 638 shows an example in which the target settings are obtained from other sources as well.

At block 640, zone controller 247 accesses the settings resolver for the selected regime zone and controls the settings resolver to resolve competing target settings into a resolved target setting. As discussed above, in some instances, the settings resolver may be a human resolver in which case zone controller 247 controls operator interface mechanisms 218 to present the competing target settings to operator 260 or another user for resolution. In some instances, the settings resolver may be a neural network or other artificial intelligence or machine learning system, and zone controller 247 submits the competing target settings to the neural network, artificial intelligence, or machine learning system for selection. In some instances, the settings resolver may be based on a predicted or historical quality metric, on threshold rules, or on logical components. In any of these latter examples, zone controller 247 executes the settings resolver to obtain a resolved target setting based on the predicted or historical quality metric, based on the threshold rules, or with the use of the logical components.

At block 642, with zone controller 247 having identified the resolved target setting, zone controller 247 provides the resolved target setting to other controllers in control system 214, which generate and apply control signals to the selected WMA or set of WMAs based upon the resolved target setting. For instance, where the selected WMA is a machine or header actuator 248, zone controller 247 provides the resolved target setting to settings controller 232 or header/real controller 238 or both to generate control signals based upon the resolved target setting, and those generated control signals are applied to the machine or header actuators 248. At block 644, if additional WMAs or additional sets of WMAs are to be controlled at the current geographic location of the agricultural harvester 100 (as detected at block 612), then processing reverts to block 626 where the next WMA or set of WMAs is selected. The processes represented by blocks 626 through 644 continue until all of the WMAs or sets of WMAs to be controlled at the current geographical location of the agricultural harvester 100 have been addressed. If no additional WMAs or sets of WMAs are to be controlled at the current geographic location of the agricultural harvester 100 remain, processing proceeds to block 646 where zone controller 247 determines whether additional control zones to be considered exist in the selected regime zone. If additional control zones to be considered exist, processing reverts to block 624 where a next control zone is selected. If no additional control zones are remaining to be considered, processing proceeds to block 648 where a determination as to whether additional regime zones are remaining to be consider. Zone controller 247 determines whether additional regime zones are remaining to be considered. If additional regimes zone are remaining to be considered, processing reverts to block 622 where a next regime zone is selected.

At block 650, zone controller 247 determines whether the operation that agricultural harvester 100 is performing is complete. If not, the zone controller 247 determines whether a control zone criterion has been satisfied to continue processing, as indicated by block 652. For instance, as mentioned above, control zone definition criteria may include criteria defining when a control zone boundary may be crossed by the agricultural harvester 100. For example, whether a control zone boundary may be crossed by the agricultural harvester 100 may be defined by a selected time period, meaning that agricultural harvester 100 is prevented from crossing a zone boundary until a selected amount of time has transpired. In that case, at block 652, zone controller 247 determines whether the selected time period has elapsed. Additionally, zone controller 247 can perform processing continually. Thus, zone controller 247 does not wait for any particular time period before continuing to determine whether an operation of the agricultural harvester 100 is completed. At block 652, zone controller 247 determines that it is time to continue processing, then processing continues at block 612 where zone controller 247 again receives an input from geographic position sensor 204. It will also be appreciated that zone controller 247 can control the WMAs and sets of WMAs simultaneously using a multiple-input, multiple-output controller instead of controlling the WMAs and sets of WMAs sequentially.

FIG. 11 is a block diagram showing one example of an operator interface controller 231. In an illustrated example, operator interface controller 231 includes operator input command processing system 654, other controller interaction system 656, speech processing system 658, and action signal generator 660. Operator input command processing system 654 includes speech handling system 662, touch gesture handling system 664, and other items 666. Other controller interaction system 656 includes controller input processing system 668 and controller output generator 670. Speech processing system 658 includes trigger detector 672, recognition component 674, synthesis component 676, natural language understanding system 678, dialog management system 680, and other items 682. Action signal generator 660 includes visual control signal generator 684, audio control signal generator 686, haptic control signal generator 688, and other items 690. Before describing operation of the example operator interface controller 231 shown in FIG. 11 in handling various operator interface actions, a brief description of some of the items in operator interface controller 231 and the associated operation thereof is first provided.

Operator input command processing system 654 detects operator inputs on operator interface mechanisms 218 and processes those inputs for commands. Speech handling system 662 detects speech inputs and handles the interactions with speech processing system 658 to process the speech inputs for commands. Touch gesture handling system 664 detects touch gestures on touch sensitive elements in operator interface mechanisms 218 and processes those inputs for commands.

Other controller interaction system 656 handles interactions with other controllers in control system 214. Controller input processing system 668 detects and processes inputs from other controllers in control system 214, and controller output generator 670 generates outputs and provides those outputs to other controllers in control system 214. Speech processing system 658 recognizes speech inputs, determines the meaning of those inputs, and provides an output indicative of the meaning of the spoken inputs. For instance, speech processing system 658 may recognize a speech input from operator 260 as a settings change command in which operator 260 is commanding control system 214 to change a setting for a controllable subsystem 216. In such an example, speech processing system 658 recognizes the content of the spoken command, identifies the meaning of that command as a settings change command, and provides the meaning of that input back to speech handling system 662. Speech handling system 662, in turn, interacts with controller output generator 670 to provide the commanded output to the appropriate controller in control system 214 to accomplish the spoken settings change command.

Speech processing system 658 may be invoked in a variety of different ways. For instance, in one example, speech handling system 662 continuously provides an input from a microphone (being one of the operator interface mechanisms 218) to speech processing system 658. The microphone detects speech from operator 260, and the speech handling system 662 provides the detected speech to speech processing system 658. Trigger detector 672 detects a trigger indicating that speech processing system 658 is invoked. In some instances, when speech processing system 658 is receiving continuous speech inputs from speech handling system 662, speech recognition component 674 performs continuous speech recognition on all speech spoken by operator 260. In some instances, speech processing system 658 is configured for invocation using a wakeup word. That is, in some instances, operation of speech processing system 658 may be initiated based on recognition of a selected spoken word, referred to as the wakeup word. In such an example, where recognition component 674 recognizes the wakeup word, the recognition component 674 provides an indication that the wakeup word has been recognized to trigger detector 672. Trigger detector 672 detects that speech processing system 658 has been invoked or triggered by the wakeup word. In another example, speech processing system 658 may be invoked by an operator 260 actuating an actuator on a user interface mechanism, such as by touching an actuator on a touch sensitive display screen, by pressing a button, or by providing another triggering input. In such an example, trigger detector 672 can detect that speech processing system 658 has been invoked when a triggering input via a user interface mechanism is detected. Trigger detector 672 can detect that speech processing system 658 has been invoked in other ways as well.

Once speech processing system 658 is invoked, the speech input from operator 260 is provided to speech recognition component 674. Speech recognition component 674 recognizes linguistic elements in the speech input, such as words, phrases, or other linguistic units. Natural language understanding system 678 identifies a meaning of the recognized speech. The meaning may be a natural language output, a command output identifying a command reflected in the recognized speech, a value output identifying a value in the recognized speech, or any of a wide variety of other outputs that reflect the understanding of the recognized speech. For example, the natural language understanding system 678 and speech processing system 568, more generally, may understand of the meaning of the recognized speech in the context of agricultural harvester 100.

In some examples, speech processing system 658 can also generate outputs that navigate operator 260 through a user experience based on the speech input. For instance, dialog management system 680 may generate and manage a dialog with the user in order to identify what the user wishes to do. The dialog may disambiguate a user's command; identify one or more specific values that are needed to carry out the user's command; or obtain other information from the user or provide other information to the user or both. Synthesis component 676 may generate speech synthesis which can be presented to the user through an audio operator interface mechanism, such as a speaker. Thus, the dialog managed by dialog management system 680 may be exclusively a spoken dialog or a combination of both a visual dialog and a spoken dialog.

Action signal generator 660 generates action signals to control operator interface mechanisms 218 based upon outputs from one or more of operator input command processing system 654, other controller interaction system 656, and speech processing system 658. Visual control signal generator 684 generates control signals to control visual items in operator interface mechanisms 218. The visual items may be lights, a display screen, warning indicators, or other visual items. Audio control signal generator 686 generates outputs that control audio elements of operator interface mechanisms 218. The audio elements include a speaker, audible alert mechanisms, horns, or other audible elements. Haptic control signal generator 688 generates control signals that are output to control haptic elements of operator interface mechanisms 218. The haptic elements include vibration elements that may be used to vibrate, for example, the operator's seat, the steering wheel, pedals, or joysticks used by the operator. The haptic elements may include tactile feedback or force feedback elements that provide tactile feedback or force feedback to the operator through operator interface mechanisms. The haptic elements may include a wide variety of other haptic elements as well.

FIG. 12 is a flow diagram illustrating one example of the operation of operator interface controller 231 in generating an operator interface display on an operator interface mechanism 218, which can include a touch sensitive display screen. FIG. 12 also illustrates one example of how operator interface controller 231 can detect and process operator interactions with the touch sensitive display screen.

At block 692, operator interface controller 231 receives a map. Block 694 indicates an example in which the map is a functional predictive map, and block 696 indicates an example in which the map is another type of map. At block 698, operator interface controller 231 receives an input from geographic position sensor 204 identifying the geographic location of the agricultural harvester 100. As indicated in block 700, the input from geographic position sensor 204 can include the heading, along with the location, of agricultural harvester 100. Block 702 indicates an example in which the input from geographic position sensor 204 includes the speed of agricultural harvester 100, and block 704 indicates an example in which the input from geographic position sensor 204 includes other items.

At block 706, visual control signal generator 684 in operator interface controller 231 controls the touch sensitive display screen in operator interface mechanisms 218 to generate a display showing all or a portion of a field represented by the received map. Block 708 indicates that the displayed field can include a current position marker showing a current position of the agricultural harvester 100 relative to the field. Block 710 indicates an example in which the displayed field includes a next work unit marker that identifies a next work unit (or area on the field) in which agricultural harvester 100 will be operating. Block 712 indicates an example in which the displayed field includes an upcoming area display portion that displays areas that are yet to be processed by agricultural harvester 100, and block 714 indicates an example in which the displayed field includes previously visited display portions that represent areas of the field that agricultural harvester 100 has already processed. Block 716 indicates an example in which the displayed field displays various characteristics of the field having georeferenced locations on the map. For instance, if the received map is a crop constituent map, such as functional predictive crop constituent map 360, the displayed field may show the different crop constituent values existing in the field georeferenced within the displayed field. The mapped characteristics can be shown in the previously visited areas (as shown in block 714), in the upcoming areas (as shown in block 712), and in the next work unit (as shown in block 710). Block 718 indicates an example in which the displayed field includes other items as well.

FIG. 13 is a pictorial illustration showing one example of a user interface display 720 that can be generated on a touch sensitive display screen. In other implementations, the user interface display 720 may be generated on other types of displays. The touch sensitive display screen may be mounted in the operator compartment of agricultural harvester 100 or on the mobile device or elsewhere. User interface display 720 will be described prior to continuing with the description of the flow diagram shown in FIG. 12.

In the example shown in FIG. 13, user interface display 720 illustrates that the touch sensitive display screen includes a display feature for operating a microphone 722 and a speaker 724. Thus, the touch sensitive display may be communicably coupled to the microphone 722 and the speaker 724. Block 726 indicates that the touch sensitive display screen can include a wide variety of user interface control actuators, such as buttons, keypads, soft keypads, links, icons, switches, etc. The operator 260 can actuator the user interface control actuators to perform various functions.

In the example shown in FIG. 13, user interface display 720 includes a field display portion 728 that displays at least a portion of the field in which the agricultural harvester 100 is operating. The field display portion 728 is shown with a current position marker 708 that corresponds to a current position of agricultural harvester 100 in the portion of the field shown in field display portion 728. In one example, the operator may control the touch sensitive display in order to zoom into portions of field display portion 728 or to pan or scroll the field display portion 728 to show different portions of the field. A next work unit 730 is shown as an area of the field directly in front of the current position marker 708 of agricultural harvester 100. The current position marker 708 may also be configured to identify the direction of travel of agricultural harvester 100, a speed of travel of agricultural harvester 100 or both. In FIG. 13, the shape of the current position marker 708 provides an indication as to the orientation of the agricultural harvester 100 within the field which may be used as an indication of a direction of travel of the agricultural harvester 100.

The size of the next work unit 730 marked on field display portion 728 may vary based upon a wide variety of different criteria. For instance, the size of next work unit 730 may vary based on the speed of travel of agricultural harvester 100. Thus, when the agricultural harvester 100 is traveling faster, then the area of the next work unit 730 may be larger than the area of next work unit 730 if agricultural harvester 100 is traveling more slowly. In another example, the size of the next work unit 730 may vary based on the dimensions of the agricultural harvester 100, including equipment on agricultural harvester 100 (such as header 102). For example, the width of the next work unit 730 may vary based on a width of header 102. Field display portion 728 is also shown displaying previously visited area 714 and upcoming areas 712. Previously visited areas 714 represent areas that are already harvested while upcoming areas 712 represent areas that still need to be harvested. The field display portion 728 is also shown displaying different characteristics of the field. In the example illustrated in FIG. 13, the map that is being displayed is a predictive crop constituent map, such as functional predictive crop constituent map 360. Therefore, a plurality of crop constituent markers are displayed on field display portion 728. There are a set of crop constituent display markers 732 shown in the already visited areas 714. There are also a set of crop constituent display markers 732 shown in the upcoming areas 712, and there are a set of crop constituent display markers 732 shown in the next work unit 730. FIG. 14 shows that the crop constituent display markers 732 are made up of different symbols that indicate an area of similar crop constituent values. In the example shown in FIG. 14, the ! symbol represents areas of high crop protein; the * symbol represents areas of medium crop protein; and the #symbol represents an area of low crop protein. Thus, the field display portion 728 shows different measured or predicted values (or characteristics indicated by the values) that are located at different areas within the field and represents those measured or predicted values (or characteristics indicated by or derived from the values) with a variety of display markers 732. As shown, the field display portion 728 includes display markers, particularly crop constituent display markers 732 in the illustrated example of FIG. 13, at particular locations associated with particular locations on the field being displayed. In some instances, each location of the field may have a display marker associated therewith. Thus, in some instances, a display marker may be provided at each location of the field display portion 728 to identify the nature of the characteristic being mapped for each particular location of the field. Consequently, the present disclosure encompasses providing a display marker, such as the crop constituent display marker 732 (as in the context of the present example of FIG. 13), at one or more locations on the field display portion 728 to identify the nature, degree, etc., of the characteristic being displayed, thereby identifying the characteristic at the corresponding location in the field being displayed. As described earlier, the display markers 732 may be made up of different symbols, and, as described below, the symbols may be any display feature such as different colors, shapes, patterns, intensities, text, icons, or other display features. In some instances, each location of the field may have a display marker associated therewith. Thus, in some instances, a display marker may be provided at each location of the field display portion 728 to identify the nature of the characteristic being mapped for each particular location of the field. Consequently, the present disclosure encompasses providing a display marker, such as the loss level display marker 732 (as in the context of the present example of FIG. 11), at one or more locations on the field display portion 728 to identify the nature, degree, etc., of the characteristic being displayed, thereby identifying the characteristic at the corresponding location in the field being displayed.

In other examples, the map being displayed may be one or more of the maps described herein, including information maps, prior information maps, the functional predictive maps, such as predictive maps or predictive control zone maps, other predictive maps, or a combination thereof. Thus, the markers and characteristics being displayed will correlate to the information, data, characteristics, and values provided by the one or more maps being displayed.

In the example of FIG. 13, user interface display 720 also has a control display portion 738. Control display portion 738 allows the operator to view information and to interact with user interface display 720 in various ways.

The actuators and display markers in portion 738 may be displayed as, for example, individual items, fixed lists, scrollable lists, drop down menus, or drop down lists. In the example shown in FIG. 13, display portion 738 shows information for the three different stalk diameter categories that correspond to the three symbols mentioned above. Display portion 738 also includes a set of touch sensitive actuators with which the operator 260 can interact by touch. For example, the operator 260 may touch the touch sensitive actuators with a finger to activate the respective touch sensitive actuator. As shown, display portion 738 also includes a number of interactive tabs, such as protein tab 762, oil tab 764, starch tab 766, multiple tab 768, and other tab 770. Activating one of the tabs can modify which values are displayed in portions 728 and 738. For instance, as shown, protein tab 762 is activated and thus the values mapped on portion 728 and shown in portion 738 correspond to protein content values of crop, or crop components, such as grain. When the operator 260 touches the tab 764, touch gesture handling system 664 updates portion 728 and 738 to display oil content values of crop, or crop components, such as grain. When the operator 260 touches the tab 766, touch gesture handling system 664 updates portion 728 and 738 to display starch content values of crop, or crop components, such as grain. When the operator 260 touches the tab 768, tough gesture handling system 664 updates portion 728 and 738 to display a combination of constituent content values, such as a combination of one or more of protein content values, oil content values, starch content values, as well as other constituent content values. When the operator 260 touches the tab 770, touch gesture handling system 664 updates portion 728 and 738 to display one or more of various other constituent content values.

As shown in FIG. 14, display portion 738 includes an interactive flag display portion, indicated generally at 741. Interactive flag display portion 741 includes a flag column 739 that shows flags that have been automatically or manually set. Flag actuator 740 allows operator 260 to mark a location, such as the current location of the agricultural harvester, or another location on the field designated by the operator and add information indicating the characteristic, such as crop moisture, found at the current location. For instance, when the operator 260 actuates the flag actuator 740 by touching the flag actuator 740, touch gesture handling system 664 in operator interface controller 231 identifies the current location as one where agricultural harvester 100 encountered high protein content. When the operator 260 touches the button 742, touch gesture handling system 664 identifies the current location as a location where agricultural harvester 100 encountered medium protein content. When the operator 260 touches the button 744, touch gesture handling system 664 identifies the current location as a location where agricultural harvester 100 encountered low protein content. Upon actuation of one of the flag actuators 740, 742, or 744, touch gesture handling system 664 can control visual control signal generator 684 to add a symbol corresponding to the identified characteristic on field display portion 728 at a location the user identifies. In this way, areas of the field where the predicted value did not accurately represent an actual value can be marked for later analysis, and can also be used in machine learning. In other examples, the operator may designate areas ahead of or around the agricultural harvester 100 by actuating one of the flag actuators 740, 742, or 744 such that control of the agricultural harvester 100 can be undertaken based on the value designated by the operator 260.

Display portion 738 also includes an interactive marker display portion, indicated generally at 743. Interactive marker display portion 743 includes a symbol column 746 that displays the symbols corresponding to each category of values or characteristics (in the case of FIG. 13, crop constituent) that is being tracked on the field display portion 728. Display portion 738 also includes an interactive designator display portion, indicated generally at 745. Interactive designator display portion 745 includes a designator column 748 that shows the designator (which may be a textual designator or other designator) identifying the category of values or characteristics (in the case of FIG. 13, crop constituent). Without limitation, the symbols in symbol column 746 and the designators in designator column 748 can include any display feature such as different colors, shapes, patterns, intensities, text, icons, or other display features, and can be customizable by interaction of an operator of agricultural harvester 100.

Display portion 738 also includes an interactive value display portion, indicated generally at 747. Interactive value display portion 747 includes a value display column 750 that displays selected values. The selected values correspond to the characteristics or values being tracked or displayed, or both, on field display portion 728. The selected values can be selected by an operator of the agricultural harvester 100. The selected values in value display column 750 define a range of values or a value by which other values, such as predicted values, are to be classified. Thus, in the example in FIG. 13, a predicted or measured crop (or crop component, such as grain) protein content meeting or greater than 12% is classified as “high crop protein” and a predicted or measured crop moisture meeting or less than 8% is classified as “low crop protein.” In some examples, the selected values may include a range, such that a predicted or measured value that is within the range of the selected value will be classified under the corresponding designator. As shown in FIG. 13, “medium crop protein” includes a range of 9%-11% such that a measured or predicted crop (or crop component, such as grain) protein content value falling within the range of 9%-11% is classified as “medium crop protein”. The selected values in value display column 750 are adjustable by an operator of agricultural harvester 100. In one example, the operator 260 can select the particular part of field display portion 728 for which the values in column 750 are to be displayed. Thus, the values in column 750 can correspond to values in display portions 712, 714 or 730.

Display portion 738 also includes an interactive threshold display portion, indicated generally at 749. Interactive threshold display portion 749 includes a threshold value display column 752 that displays action threshold values. Action threshold values in column 752 may be threshold values corresponding to the selected values in value display column 750. If the predicted or measured values of characteristics being tracked or displayed, or both, satisfy the corresponding action threshold values in threshold value display column 752, then control system 214 takes one or more actions identified in column 754. In some instances, a measured or predicted value may satisfy a corresponding action threshold value by meeting or exceeding the corresponding action threshold value. In one example, operator 260 can select a threshold value, for example, in order to change the threshold value by touching the threshold value in threshold value display column 752. Once selected, the operator 260 may change the threshold value. The threshold values in column 752 can be configured such that the designated action is performed when the measured or predicted value of the characteristic exceeds the threshold value, equals the threshold value, or is less than the threshold value. In some instances, the threshold value may represent a range of values, or range of deviation from the selected values in value display column 750, such that a predicted or measured characteristic value that meets or falls within the range satisfies the threshold value. For instance, in the example of crop constituent, a predicted crop (or crop component, such as grain) protein content value that falls within 0.5% of 12% protein content will satisfy the corresponding action threshold value (of within 0.5% of 12% protein content) and an action, such as adjusting the cleaning fan speed of the agricultural harvester or adjusting the chaffer, will be taken by control system 214. In other examples, the threshold values in column threshold value display column 752 are separate from the selected values in value display column 750, such that the values in value display column 750 define the classification and display of predicted or measured values, while the action threshold values define when an action is to be taken based on the measured or predicted values. For example, while a predicted or measured crop (or crop component, such as grain) protein content of 12% may be designated as “high crop protein” for purposes of classification and display, the action threshold value may be 13% such that no action will be taken until the crop (or crop component, such as grain) protein content satisfies the threshold value. In other examples, the threshold values in threshold value display column 752 may include distances or times. For instance, in the example of a distance, the threshold value may be a threshold distance from the area of the field where the measured or predicted value is georeferenced that the agricultural harvester 100 must be before an action is taken. For example, a threshold distance value of 5 feet would mean that an action will be taken when the agricultural harvester is at or within 5 feet of the area of the field where the measured or predicted value is georeferenced. In an example where the threshold value is time, the threshold value may be a threshold time for the agricultural harvester 100 to reach the area of the field where the measured or predictive value is georeferenced. For instance, a threshold value of 5 seconds would mean that an action will be taken when the agricultural harvester 100 is 5 seconds away from the area of the field where the measured or predicted value is georeferenced. In such an example, the current location and travel speed of the agricultural harvester can be accounted for.

Display portion 738 also includes an interactive action display portion, indicated generally at 751. Interactive action display portion 751 includes an action display column 754 that displays action identifiers that indicate actions to be taken when a predicted or measured value satisfies an action threshold value in threshold value display column 752. Operator 260 can touch the action identifiers in column 754 to change the action that is to be taken. When a threshold is satisfied, an action may be taken. For instance, at the bottom of column 754, an chaffer action, an adjust cleaning fan speed action, and a keep out action are identified as actions that will be taken if the measured or predicted value meets the threshold value in column 752. In some examples, when a threshold is met, multiple actions may be taken. For instance, a speed of the cleaning fan may be adjusted (such as increased or decreased) and the size of the openings in the chaffer may be adjusted (such opened or closed). These are merely some examples.

The actions that can be set in column 754 can be any of a wide variety of different types of actions. For example, the actions can include a keep out action which, when executed, inhibits agricultural harvester 100 from further harvesting in an area. The actions can include a speed change action which, when executed, changes the travel speed of agricultural harvester 100 through the field. The actions can include a setting change action for changing a setting of an internal actuator or another WMA or set of WMAs or for implementing a settings change action that changes a setting, such as a cleaning fan speed setting or a chaffer clearance setting along with various other settings. These are examples only, and a wide variety of other actions are contemplated herein.

The items shown on user interface display 720 can be visually controlled. Visually controlling the interface display 720 may be performed to capture the attention of operator 260. For instance, the items can be controlled to modify the intensity, color, or pattern with which the items are displayed. Additionally, the items may be controlled to flash. The described alterations to the visual appearance of the items are provided as examples. Consequently, other aspects of the visual appearance of the items may be altered. Therefore, the items can be modified under various circumstances in a desired manner in order, for example, to capture the attention of operator 260. Additionally, while a particular number of items are shown on user interface display 720, this need not be the case. In other examples, more or less items, including more or less of a particular item can be included on user interface display 720.

Returning now to the flow diagram of FIG. 12, the description of the operation of operator interface controller 231 continues. At block 760, operator interface controller 231 detects an input setting a flag and controls the touch sensitive user interface display 720 to display the flag on field display portion 728. The detected input may be an operator input, as indicated at 762, or an input from another controller, as indicated at 764. At block 766, operator interface controller 231 detects an in-situ sensor input indicative of a measured characteristic of the field from one of the in-situ sensors 208. At block 768, visual control signal generator 684 generates control signals to control user interface display 720 to display actuators for modifying user interface display 720 and for modifying machine control. For instance, block 770 represents that one or more of the actuators for setting or modifying the values in columns 739, 746, and 748 can be displayed. Thus, the user can set flags and modify characteristics of those flags. Block 772 represents that action threshold values in column 752 are displayed. Block 776 represents that the actions in column 754 are displayed, and block 778 represents that the selected value in column 750 is displayed. Block 780 indicates that a wide variety of other information and actuators can be displayed on user interface display 720 as well.

At block 782, operator input command processing system 654 detects and processes operator inputs corresponding to interactions with the user interface display 720 performed by the operator 260. Where the user interface mechanism on which user interface display 720 is displayed is a touch sensitive display screen, interaction inputs with the touch sensitive display screen by the operator 260 can be touch gestures 784. In some instances, the operator interaction inputs can be inputs using a point and click device 786 or other operator interaction inputs 788.

At block 790, operator interface controller 231 receives signals indicative of an alert condition. For instance, block 792 indicates that signals may be received by controller input processing system 668 indicating that measured or predicted values satisfy threshold conditions present in column 752. As explained earlier, the threshold conditions may include values being below a threshold, at a threshold, or above a threshold. Block 794 shows that action signal generator 660 can, in response to receiving an alert condition, alert the operator 260 by using visual control signal generator 684 to generate visual alerts, by using audio control signal generator 686 to generate audio alerts, by using haptic control signal generator 688 to generate haptic alerts, or by using any combination of these. Similarly, as indicated by block 796, controller output generator 670 can generate outputs to other controllers in control system 214 so that those controllers perform the corresponding action identified in column 754. Block 798 shows that operator interface controller 231 can detect and process alert conditions in other ways as well.

Block 900 shows that speech handling system 662 may detect and process inputs invoking speech processing system 658. Block 902 shows that performing speech processing may include the use of dialog management system 680 to conduct a dialog with the operator 260. Block 904 shows that the speech processing may include providing signals to controller output generator 670 so that control operations are automatically performed based upon the speech inputs.

Table 1, below, shows an example of a dialog between operator interface controller 231 and operator 260. In Table 1, operator 260 uses a trigger word or a wakeup word that is detected by trigger detector 672 to invoke speech processing system 658. In the example shown in Table 1, the wakeup word is “Johnny”.

TABLE 1 Operator: “Johnny, tell me about current crop constituent values.” Operator Interface Controller: “Protein content is currently high.” Operator: “Johnny, what should I do because of the protein content?” Operator Interface Controller: “Reduce travel speed by 1 mph to reduce grain loss.”

Table 2 shows an example in which speech synthesis component 676 provides an output to audio control signal generator 686 to provide audible updates on an intermittent or periodic basis. The interval between updates may be time-based, such as every five minutes, or coverage or distance-based, such as every five acres, or exception-based, such as when a measured value is greater than a threshold value.

TABLE 2  Operator Interface Controller: “Over last 10 minutes, starch content has been high.”  Operator Interface Controller: “Next 1 acre predicted starch content is medium.”  Operator Interface Controller: “Caution: upcoming change in starch content, open chaffer to capture more grain.”

The example shown in Table 3 illustrates that some actuators or user input mechanisms on the touch sensitive display 720 can be supplemented with speech dialog. The example in Table 3 illustrates that action signal generator 660 can generate action signals to automatically mark a crop constituent area in the field being harvested.

TABLE 3 Human: “Johnny, mark high oil content area.” Operator Interface Controller: “High oil content area marked.”

The example shown in Table 4 illustrates that action signal generator 660 can conduct a dialog with operator 260 to begin and end marking of a crop constituent area.

TABLE 4 Human: “Johnny, start marking high oil content area.” Operator Interface Controller: “Marking high oil content area.” Human: “Johnny, stop marking high oil content area.” Operator Interface Controller: “High oil content area marking stopped.”

The example shown in Table 5 illustrates that action signal generator 160 can generate signals to mark a crop constituent area in a different way than those shown in Tables 3 and 4.

TABLE 5  Human: “Johnny, mark next 100 feet as a low protein content area.”  Operator Interface Controller: “Next 100 feet marked as a low protein content area.”

Returning again to FIG. 12, block 906 illustrates that operator interface controller 231 can detect and process conditions for outputting a message or other information in other ways as well. For instance, other controller interaction system 656 can detect inputs from other controllers indicating that alerts or output messages should be presented to operator 260. Block 908 shows that the outputs can be audio messages. Block 910 shows that the outputs can be visual messages, and block 912 shows that the outputs can be haptic messages. Until operator interface controller 231 determines that the current harvesting operation is completed, as indicated by block 914, processing reverts to block 698 where the geographic location of harvester 100 is updated and processing proceeds as described above to update user interface display 720.

Once the operation is complete, then any desired values that are displayed, or have been displayed on user interface display 720, can be saved. Those values can also be used in machine learning to improve different portions of predictive model generator 210, predictive map generator 212, control zone generator 213, control algorithms, or other items. Saving the desired values is indicated by block 916. The values can be saved locally on agricultural harvester 100, or the values can be saved at a remote server location or sent to another remote system.

It can thus be seen that one or more maps are obtained by an agricultural harvester that show agricultural characteristic values, including predicted or historical crop constituent values, prior operation characteristic values, vegetative index values, or soil composition values, at different geographic locations of a field being harvested. An in-situ sensor on the harvester senses a characteristic that has values indicative of an agricultural characteristic as the agricultural harvester moves through the field. A predictive map generator generates a predictive map that predicts control values for different locations in the field based on the values of the agricultural characteristic in the map and the agricultural characteristic sensed by the in-situ sensor. A control system controls controllable subsystem based on the control values in the predictive map.

A control value is a value upon which an action can be based. A control value, as described herein, can include any value (or characteristics indicated by or derived from the value) that may be used in the control of agricultural harvester 100. A control value can be any value indicative of an agricultural characteristic. A control value can be a predicted value, a measured value, or a detected value. A control value may include any of the values provided by a map, such as any of the maps described herein, for instance, a control value can be a value provided by an information map, a value provided by prior information map, or a value provided predictive map, such as a functional predictive map. A control value can also include any of the characteristics indicated by or derived from the values detected by any of the sensors described herein. In other examples, a control value can be provided by an operator of the agricultural machine, such as a command input by an operator of the agricultural machine.

The present discussion has mentioned processors and servers. In some examples, the processors and servers include computer processors with associated memory and timing circuitry, not separately shown. The processors and servers are functional parts of the systems or devices to which the processors and servers belong and are activated by and facilitate the functionality of the other components or items in those systems.

Also, a number of user interface displays have been discussed. The displays can take a wide variety of different forms and can have a wide variety of different user actuatable operator interface mechanisms disposed thereon. For instance, user actuatable operator interface mechanisms may include text boxes, check boxes, icons, links, drop-down menus, search boxes, etc. The user actuatable operator interface mechanisms can also be actuated in a wide variety of different ways. For instance, the user actuatable operator interface mechanisms can be actuated using operator interface mechanisms such as a point and click device, such as a track ball or mouse, hardware buttons, switches, a joystick or keyboard, thumb switches or thumb pads, etc., a virtual keyboard or other virtual actuators. In addition, where the screen on which the user actuatable operator interface mechanisms are displayed is a touch sensitive screen, the user actuatable operator interface mechanisms can be actuated using touch gestures. Also, user actuatable operator interface mechanisms can be actuated using speech commands using speech recognition functionality. Speech recognition may be implemented using a speech detection device, such as a microphone, and software that functions to recognize detected speech and execute commands based on the received speech.

A number of data stores have also been discussed. It will be noted the data stores can each be broken into multiple data stores. In some examples, one or more of the data stores may be local to the systems accessing the data stores, one or more of the data stores may all be located remote form a system utilizing the data store, or one or more data stores may be local while others are remote. All of these configurations are contemplated by the present disclosure.

Also, the figures show a number of blocks with functionality ascribed to each block. It will be noted that fewer blocks can be used to illustrate that the functionality ascribed to multiple different blocks is performed by fewer components. Also, more blocks can be used illustrating that the functionality may be distributed among more components. In different examples, some functionality may be added, and some may be removed.

It will be noted that the above discussion has described a variety of different systems, components, logic, and interactions. It will be appreciated that any or all of such systems, components, logic and interactions may be implemented by hardware items, such as processors, memory, or other processing components, including but not limited to artificial intelligence components, such as neural networks, some of which are described below, that perform the functions associated with those systems, components, logic, or interactions. In addition, any or all of the systems, components, logic and interactions may be implemented by software that is loaded into a memory and is subsequently executed by a processor or server or other computing component, as described below. Any or all of the systems, components, logic and interactions may also be implemented by different combinations of hardware, software, firmware, etc., some examples of which are described below. These are some examples of different structures that may be used to implement any or all of the systems, components, logic and interactions described above. Other structures may be used as well.

FIG. 14 is a block diagram of agricultural harvester 600, which may be similar to agricultural harvester 100 shown in FIG. 2. The agricultural harvester 600 communicates with elements in a remote server architecture 500. In some examples, remote server architecture 500 provides computation, software, data access, and storage services that do not require end-user knowledge of the physical location or configuration of the system that delivers the services. In various examples, remote servers may deliver the services over a wide area network, such as the internet, using appropriate protocols. For instance, remote servers may deliver applications over a wide area network and may be accessible through a web browser or any other computing component. Software or components shown in FIG. 2 as well as data associated therewith, may be stored on servers at a remote location. The computing resources in a remote server environment may be consolidated at a remote data center location, or the computing resources may be dispersed to a plurality of remote data centers. Remote server infrastructures may deliver services through shared data centers, even though the services appear as a single point of access for the user. Thus, the components and functions described herein may be provided from a remote server at a remote location using a remote server architecture. Alternatively, the components and functions may be provided from a server, or the components and functions can be installed on client devices directly, or in other ways.

In the example shown in FIG. 14, some items are similar to those shown in FIG. 2 and those items are similarly numbered. FIG. 14 specifically shows that predictive model generator 210 or predictive map generator 212, or both, may be located at a server location 502 that is remote from the agricultural harvester 600. Therefore, in the example shown in FIG. 14, agricultural harvester 600 accesses systems through remote server location 502.

FIG. 14 also depicts another example of a remote server architecture. FIG. 14 shows that some elements of FIG. 2 may be disposed at a remote server location 502 while others may be located elsewhere. By way of example, data store 202 may be disposed at a location separate from location 502 and accessed via the remote server at location 502. Regardless of where the elements are located, the elements can be accessed directly by agricultural harvester 600 through a network such as a wide area network or a local area network; the elements can be hosted at a remote site by a service; or the elements can be provided as a service or accessed by a connection service that resides in a remote location. Also, data may be stored in any location, and the stored data may be accessed by, or forwarded to, operators, users, or systems. For instance, physical carriers may be used instead of, or in addition to, electromagnetic wave carriers. In some examples, where wireless telecommunication service coverage is poor or nonexistent, another machine, such as a fuel truck or other mobile machine or vehicle, may have an automated, semi-automated, or manual information collection system. As the combine harvester 600 comes close to the machine containing the information collection system, such as a fuel truck prior to fueling, the information collection system collects the information from the combine harvester 600 using any type of ad-hoc wireless connection. The collected information may then be forwarded to another network when the machine containing the received information reaches a location where wireless telecommunication service coverage or other wireless coverage is available. For instance, a fuel truck may enter an area having wireless communication coverage when traveling to a location to fuel other machines or when at a main fuel storage location. All of these architectures are contemplated herein. Further, the information may be stored on the agricultural harvester 600 until the agricultural harvester 600 enters an area having wireless communication coverage. The agricultural harvester 600, itself, may send the information to another network.

It will also be noted that the elements of FIG. 2, or portions thereof, may be disposed on a wide variety of different devices. One or more of those devices may include an on-board computer, an electronic control unit, a display unit, a server, a desktop computer, a laptop computer, a tablet computer, or other mobile device, such as a palm top computer, a cell phone, a smart phone, a multimedia player, a personal digital assistant, etc.

In some examples, remote server architecture 500 may include cybersecurity measures. Without limitation, these measures may include encryption of data on storage devices, encryption of data sent between network nodes, authentication of people or processes accessing data, as well as the use of ledgers for recording metadata, data, data transfers, data accesses, and data transformations. In some examples, the ledgers may be distributed and immutable (e.g., implemented as blockchain).

FIG. 15 is a simplified block diagram of one illustrative example of a handheld or mobile computing device that can be used as a user's or client's hand held device 16, in which the present system (or parts of it) can be deployed. For instance, a mobile device can be deployed in the operator compartment of agricultural harvester 100 for use in generating, processing, or displaying the maps discussed above. FIGS. 16-17 are examples of handheld or mobile devices.

FIG. 15 provides a general block diagram of the components of a client device 16 that can run some components shown in FIG. 2, that interacts with them, or both. In the device 16, a communications link 13 is provided that allows the handheld device to communicate with other computing devices and under some examples provides a channel for receiving information automatically, such as by scanning. Examples of communications link 13 include allowing communication though one or more communication protocols, such as wireless services used to provide cellular access to a network, as well as protocols that provide local wireless connections to networks.

In other examples, applications can be received on a removable Secure Digital (SD) card that is connected to an interface 15. Interface 15 and communication links 13 communicate with a processor 17 (which can also embody processors or servers from other FIGS.) along a bus 19 that is also connected to memory 21 and input/output (I/O) components 23, as well as clock 25 and location system 27.

I/O components 23, in one example, are provided to facilitate input and output operations. I/O components 23 for various examples of the device 16 can include input components such as buttons, touch sensors, optical sensors, microphones, touch screens, proximity sensors, accelerometers, orientation sensors and output components such as a display device, a speaker, and or a printer port. Other I/O components 23 can be used as well.

Clock 25 illustratively comprises a real time clock component that outputs a time and date. It can also, illustratively, provide timing functions for processor 17.

Location system 27 illustratively includes a component that outputs a current geographical location of device 16. This can include, for instance, a global positioning system (GPS) receiver, a LORAN system, a dead reckoning system, a cellular triangulation system, or other positioning system. Location system 27 can also include, for example, mapping software or navigation software that generates desired maps, navigation routes and other geographic functions.

Memory 21 stores operating system 29, network settings 31, applications 33, application configuration settings 35, data store 37, communication drivers 39, and communication configuration settings 41. Memory 21 can include all types of tangible volatile and non-volatile computer-readable memory devices. Memory 21 may also include computer storage media (described below). Memory 21 stores computer readable instructions that, when executed by processor 17, cause the processor to perform computer-implemented steps or functions according to the instructions. Processor 17 may be activated by other components to facilitate their functionality as well.

FIG. 16 shows one example in which device 16 is a tablet computer 600. In FIG. 16, computer 601 is shown with user interface display screen 602. Screen 602 can be a touch screen or a pen-enabled interface that receives inputs from a pen or stylus. Tablet computer 600 may also use an on-screen virtual keyboard. Of course, computer 601 might also be attached to a keyboard or other user input device through a suitable attachment mechanism, such as a wireless link or USB port, for instance. Computer 601 may also illustratively receive voice inputs as well.

FIG. 17 is similar to FIG. 16 except that the device is a smart phone 71. Smart phone 71 has a touch sensitive display 73 that displays icons or tiles or other user input mechanisms 75. Mechanisms 75 can be used by a user to run applications, make calls, perform data transfer operations, etc. In general, smart phone 71 is built on a mobile operating system and offers more advanced computing capability and connectivity than a feature phone.

Note that other forms of the devices 16 are possible.

FIG. 18 is one example of a computing environment in which elements of FIG. 2 can be deployed. With reference to FIG. 18, an example system for implementing some embodiments includes a computing device in the form of a computer 810 programmed to operate as discussed above. Components of computer 810 may include, but are not limited to, a processing unit 820 (which can comprise processors or servers from previous FIGS.), a system memory 830, and a system bus 821 that couples various system components including the system memory to the processing unit 820. The system bus 821 may be any of several types of bus structures including a memory bus or memory controller, a peripheral bus, and a local bus using any of a variety of bus architectures. Memory and programs described with respect to FIG. 2 can be deployed in corresponding portions of FIG. 18.

Computer 810 typically includes a variety of computer readable media. Computer readable media may be any available media that can be accessed by computer 810 and includes both volatile and nonvolatile media, removable and non-removable media. By way of example, and not limitation, computer readable media may comprise computer storage media and communication media. Computer storage media is different from, and does not include, a modulated data signal or carrier wave. Computer readable media includes hardware storage media including both volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by computer 810. Communication media may embody computer readable instructions, data structures, program modules or other data in a transport mechanism and includes any information delivery media. The term “modulated data signal” means a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal.

The system memory 830 includes computer storage media in the form of volatile and/or nonvolatile memory or both such as read only memory (ROM) 831 and random access memory (RAM) 832. A basic input/output system 833 (BIOS), containing the basic routines that help to transfer information between elements within computer 810, such as during start-up, is typically stored in ROM 831. RAM 832 typically contains data or program modules or both that are immediately accessible to and/or presently being operated on by processing unit 820. By way of example, and not limitation, FIG. 18 illustrates operating system 834, application programs 835, other program modules 836, and program data 837.

The computer 810 may also include other removable/non-removable volatile/nonvolatile computer storage media. By way of example only, FIG. 18 illustrates a hard disk drive 841 that reads from or writes to non-removable, nonvolatile magnetic media, an optical disk drive 855, and nonvolatile optical disk 856. The hard disk drive 841 is typically connected to the system bus 821 through a non-removable memory interface such as interface 840, and optical disk drive 855 are typically connected to the system bus 821 by a removable memory interface, such as interface 850.

Alternatively, or in addition, the functionality described herein can be performed, at least in part, by one or more hardware logic components. For example, and without limitation, illustrative types of hardware logic components that can be used include Field-programmable Gate Arrays (FPGAs), Application-specific Integrated Circuits (e.g., ASICs), Application-specific Standard Products (e.g., ASSPs), System-on-a-chip systems (SOCs), Complex Programmable Logic Devices (CPLDs), etc.

The drives and their associated computer storage media discussed above and illustrated in FIG. 18, provide storage of computer readable instructions, data structures, program modules and other data for the computer 810. In FIG. 18, for example, hard disk drive 841 is illustrated as storing operating system 844, application programs 845, other program modules 846, and program data 847. Note that these components can either be the same as or different from operating system 834, application programs 835, other program modules 836, and program data 837.

A user may enter commands and information into the computer 810 through input devices such as a keyboard 862, a microphone 863, and a pointing device 861, such as a mouse, trackball or touch pad. Other input devices (not shown) may include a joystick, game pad, satellite dish, scanner, or the like. These and other input devices are often connected to the processing unit 820 through a user input interface 860 that is coupled to the system bus, but may be connected by other interface and bus structures. A visual display 891 or other type of display device is also connected to the system bus 821 via an interface, such as a video interface 890. In addition to the monitor, computers may also include other peripheral output devices such as speakers 897 and printer 896, which may be connected through an output peripheral interface 895.

The computer 810 is operated in a networked environment using logical connections (such as a controller area network—CAN, local area network—LAN, or wide area network WAN) to one or more remote computers, such as a remote computer 880.

When used in a LAN networking environment, the computer 810 is connected to the LAN 871 through a network interface or adapter 870. When used in a WAN networking environment, the computer 810 typically includes a modem 872 or other means for establishing communications over the WAN 873, such as the Internet. In a networked environment, program modules may be stored in a remote memory storage device. FIG. 18 illustrates, for example, that remote application programs 885 can reside on remote computer 880.

It should also be noted that the different examples described herein can be combined in different ways. That is, parts of one or more examples can be combined with parts of one or more other examples. All of this is contemplated herein.

Example 1 is an agricultural work machine, comprising:

a communication system that receives a map that includes values of an agricultural characteristic corresponding to different geographic locations in a field;

a geographic position sensor that detects a geographic location of the agricultural work machine;

an in-situ sensor that detects a value of a crop constituent corresponding to the geographic location;

a predictive model generator that generates a predictive agricultural model that models a relationship between the agricultural characteristic and the crop constituent based on a value of the agricultural characteristic in the map at the geographic location and the value of the crop constituent detected by the in-situ sensor corresponding to the geographic location; and

a predictive map generator that generates a functional predictive agricultural map of the field that maps predictive values of the crop constituent to the different geographic locations in the field, based on the values of the agricultural characteristic in the map and based on the predictive agricultural model.

Example 2 is the agricultural work machine of any or all previous examples, wherein the predictive map generator configures the functional predictive agricultural map for consumption by a control system that generates control signals to control a controllable subsystem on the agricultural work machine based on the functional predictive agricultural map.

Example 3 is the agricultural work machine of any or all previous examples, wherein the control signals control the controllable subsystem to control a speed of a cleaning fan on the agricultural work machine.

Example 4 is the agricultural work machine of any or all previous examples, wherein the control signals control the controllable subsystem to control a size of openings on a chaffer on the agricultural work machine.

Example 5 is the agricultural work machine of any or all previous examples, wherein the map comprises a prior vegetative index map that includes, as the values of the agricultural characteristic, vegetative index values corresponding to the different geographic locations in the field.

Example 6 is the agricultural work machine of any or all previous examples, wherein the predictive model generator is configured to identify a relationship between the crop constituent and the vegetative index values based on the value of the crop constituent detected by the in-situ sensor corresponding to the geographic location and a vegetative index value, in the vegetative index map, at the geographic location, the predictive agricultural model being configured to receive a vegetative index value as a model input and generate a predictive value of the crop constituent as a model output based on the identified relationship

Example 7 is the agricultural work machine of any or all previous examples, wherein the map comprises a historical crop constituent map that includes, as the values of the agricultural characteristic, historical values of the crop constituent corresponding to the different geographic locations in the field.

Example 8 is the agricultural work machine of any or all previous examples, wherein the predictive model generator is configured to identify a relationship between the crop constituent and the historical values of the crop constituent based on the values of the crop constituent detected by the in-situ sensor corresponding to the geographic location and a historical value of the crop constituent, in the historical crop constituent map, at the geographic location, the predictive agricultural model being configured to receive a historical value of the crop constituent as a model input and generate a predictive value of the crop constituent as a model output based on the identified relationship.

Example 9 is the agricultural work machine of any or all previous examples, wherein the map comprises a predictive map that maps, as the values of the agricultural characteristic, predictive values of the agricultural characteristic corresponding to the different geographic locations in the field.

Example 10 is the agricultural work machine of any or all previous examples, wherein the predictive model generator is configured to identify a relationship between the crop constituent and the predictive values of the agricultural characteristic, based on the value of the crop constituent detected by the in-situ sensor corresponding to the geographic location and a predictive agricultural characteristic value, in the predictive map, at the geographic location, the predictive agricultural model being configured to receive a predictive agricultural characteristic value as a model input and generate a predictive value of the crop constituent as a model output based on the identified relationship.

Example 11 is the agricultural work machine of any or all previous examples, wherein the map comprises a soil property map that maps, as the values of the agricultural characteristic, values of a soil property corresponding to the different geographic locations in the field.

Example 12 is the agricultural work machine of any or all previous examples, wherein the predictive model generator is configured to identify a relationship between the crop constituent and the soil property based on the value of the crop constituent detected by the in-situ sensor corresponding to the geographic location and a value of the soil property, in the soil property map, at the geographic location, the predictive agricultural model being configured to receive a value of the soil property as a model input and generate a predictive value of the crop constituent as a model output based on the identified relationship.

Example 13 is a computer implemented method of generating a functional predictive agricultural map, comprising:

receiving, at an agricultural work machine, a map that includes values of an agricultural characteristic corresponding to different geographic locations in a field;

detecting a geographic location of the agricultural work machine;

detecting, with an in-situ sensor, a value of a crop constituent corresponding to the geographic location;

generating a predictive agricultural model that models a relationship between the agricultural characteristic and the crop constituent; and

controlling a predictive map generator to generate the functional predictive agricultural map of the field that maps predictive values of the crop constituent to the different locations in the field based on the values of the agricultural characteristic in the map and the predictive agricultural model.

Example 14 is the computer implemented method of any or all previous examples, and further comprising:

configuring the functional predictive agricultural map for a control system that generates control signals to control a controllable subsystem on the agricultural work machine based on the functional predictive agricultural map.

Example 15 is the computer implemented method of any or all previous examples, wherein receiving the prior information map comprises receiving a prior vegetative index map that includes, as the values of the agricultural characteristic, vegetative index values corresponding to the different geographic locations in the field.

Example 16 is the computer implemented method of any or all previous examples, wherein generating a predictive agricultural model comprises:

identifying a relationship between the vegetative index and the crop constituent based on the value of the crop constituent corresponding to the geographic location and a vegetative index value, in the vegetative index map, at the geographic location; and

controlling a predictive model generator to generate the predictive agricultural model that receives a vegetative index value as a model input and generates a predictive value of the crop constituent as a model output based on the identified relationship.

Example 17 is the computer implemented method of any or all previous examples, wherein receiving the prior information map comprises receiving a historical crop constituent map that includes, as the agricultural characteristic, historical values of the crop constituent corresponding to the different geographic locations in the field.

Example 18 is the computer implemented method of any or all previous examples, wherein generating a predictive agricultural model comprises:

identifying a relationship between the historical crop constituent values and the crop constituent based on the value of the crop constituent corresponding to the geographic location and a historical value of the crop constituent, in the historical crop constituent map, at the geographic location; and

controlling a predictive model generator to generate the predictive agricultural model that receives a historical value of the crop constituent as a model input and generates a predictive value of the crop constituent as a model output based on the identified relationship.

Example 19 is the computer implemented method of any or all previous examples, further comprising:

controlling an operator interface mechanism to present the functional predictive agricultural map.

Example 20 is an agricultural work machine, comprising:

a communication system that receives a map that indicates agricultural characteristic values corresponding to different geographic locations in a field;

a geographic position sensor that detects a geographic location of the agricultural work machine;

an in-situ sensor that detects a value of a crop constituent corresponding to the geographic location;

a predictive model generator that generates a predictive crop constituent model that models a relationship between the agricultural characteristic and the crop constituent based on an agricultural characteristic value in the map at the geographic location and the value of the crop constituent detected by the in-situ sensor corresponding to the geographic location; and

a predictive map generator that generates a functional predictive crop constituent map of the field, that maps predictive values of the crop constituent to the different locations in the field, based on the agricultural characteristic values in the map and based on the predictive crop constituent model.

Although the subject matter has been described in language specific to structural features or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of the claims. 

What is claimed is:
 1. An agricultural work machine, comprising: a communication system that receives a map that includes values of an agricultural characteristic corresponding to different geographic locations in a field; a geographic position sensor that detects a geographic location of the agricultural work machine; an in-situ sensor that detects a value of a crop constituent corresponding to the geographic location; a predictive model generator that generates a predictive agricultural model that models a relationship between the agricultural characteristic and the crop constituent based on a value of the agricultural characteristic in the map at the geographic location and the value of the crop constituent detected by the in-situ sensor corresponding to the geographic location; and a predictive map generator that generates a functional predictive agricultural map of the field that maps predictive values of the crop constituent to the different geographic locations in the field, based on the values of the agricultural characteristic in the map and based on the predictive agricultural model.
 2. The agricultural work machine of claim 1, wherein the predictive map generator configures the functional predictive agricultural map for consumption by a control system that generates control signals to control a controllable subsystem on the agricultural work machine based on the functional predictive agricultural map.
 3. The agricultural work machine of claim 2, wherein the control signals control the controllable subsystem to control a speed of a cleaning fan on the agricultural work machine.
 4. The agricultural work machine of claim 2, wherein the control signals control the controllable subsystem to control a size of openings on a chaffer on the agricultural work machine.
 5. The agricultural work machine of claim 1, wherein the map comprises a prior vegetative index map that includes, as the values of the agricultural characteristic, vegetative index values corresponding to the different geographic locations in the field.
 6. The agricultural work machine of claim 5, wherein the predictive model generator is configured to identify a relationship between the crop constituent and the vegetative index values based on the value of the crop constituent detected by the in-situ sensor corresponding to the geographic location and a vegetative index value, in the vegetative index map, at the geographic location, the predictive agricultural model being configured to receive a vegetative index value as a model input and generate a predictive value of the crop constituent as a model output based on the identified relationship
 7. The agricultural work machine of claim 1, wherein the map comprises a historical crop constituent map that includes, as the values of the agricultural characteristic, historical values of the crop constituent corresponding to the different geographic locations in the field.
 8. The agricultural work machine of claim 7, wherein the predictive model generator is configured to identify a relationship between the crop constituent and the historical values of the crop constituent based on the values of the crop constituent detected by the in-situ sensor corresponding to the geographic location and a historical value of the crop constituent, in the historical crop constituent map, at the geographic location, the predictive agricultural model being configured to receive a historical value of the crop constituent as a model input and generate a predictive value of the crop constituent as a model output based on the identified relationship.
 9. The agricultural work machine of claim 1, wherein the map comprises a predictive map that maps, as the values of the agricultural characteristic, predictive values of the agricultural characteristic corresponding to the different geographic locations in the field.
 10. The agricultural work machine of claim 9, wherein the predictive model generator is configured to identify a relationship between the crop constituent and the predictive values of the agricultural characteristic, based on the value of the crop constituent detected by the in-situ sensor corresponding to the geographic location and a predictive agricultural characteristic value, in the predictive map, at the geographic location, the predictive agricultural model being configured to receive a predictive agricultural characteristic value as a model input and generate a predictive value of the crop constituent as a model output based on the identified relationship.
 11. The agricultural work machine of claim 1, wherein the map comprises a soil property map that maps, as the values of the agricultural characteristic, values of a soil property corresponding to the different geographic locations in the field.
 12. The agricultural work machine of claim 11, wherein the predictive model generator is configured to identify a relationship between the crop constituent and the soil property based on the value of the crop constituent detected by the in-situ sensor corresponding to the geographic location and a value of the soil property, in the soil property map, at the geographic location, the predictive agricultural model being configured to receive a value of the soil property as a model input and generate a predictive value of the crop constituent as a model output based on the identified relationship.
 13. A computer implemented method of generating a functional predictive agricultural map, comprising: receiving, at an agricultural work machine, a map that includes values of an agricultural characteristic corresponding to different geographic locations in a field; detecting a geographic location of the agricultural work machine; detecting, with an in-situ sensor, a value of a crop constituent corresponding to the geographic location; generating a predictive agricultural model that models a relationship between the agricultural characteristic and the crop constituent; and controlling a predictive map generator to generate the functional predictive agricultural map of the field that maps predictive values of the crop constituent to the different locations in the field based on the values of the agricultural characteristic in the map and the predictive agricultural model.
 14. The computer implemented method of claim 13, and further comprising: configuring the functional predictive agricultural map for a control system that generates control signals to control a controllable subsystem on the agricultural work machine based on the functional predictive agricultural map.
 15. The computer implemented method of claim 13, wherein receiving the prior information map comprises receiving a prior vegetative index map that includes, as the values of the agricultural characteristic, vegetative index values corresponding to the different geographic locations in the field.
 16. The computer implemented method of claim 15, wherein generating a predictive agricultural model comprises: identifying a relationship between the vegetative index and the crop constituent based on the value of the crop constituent corresponding to the geographic location and a vegetative index value, in the vegetative index map, at the geographic location; and controlling a predictive model generator to generate the predictive agricultural model that receives a vegetative index value as a model input and generates a predictive value of the crop constituent as a model output based on the identified relationship.
 17. The computer implemented method of claim 13, wherein receiving the prior information map comprises receiving a historical crop constituent map that includes, as the agricultural characteristic, historical values of the crop constituent corresponding to the different geographic locations in the field.
 18. The computer implemented method of claim 17, wherein generating a predictive agricultural model comprises: identifying a relationship between the historical crop constituent values and the crop constituent based on the value of the crop constituent corresponding to the geographic location and a historical value of the crop constituent, in the historical crop constituent map, at the geographic location; and controlling a predictive model generator to generate the predictive agricultural model that receives a historical value of the crop constituent as a model input and generates a predictive value of the crop constituent as a model output based on the identified relationship.
 19. The computer implemented method of claim 13, further comprising: controlling an operator interface mechanism to present the functional predictive agricultural map.
 20. An agricultural work machine, comprising: a communication system that receives a map that indicates agricultural characteristic values corresponding to different geographic locations in a field; a geographic position sensor that detects a geographic location of the agricultural work machine; an in-situ sensor that detects a value of a crop constituent corresponding to the geographic location; a predictive model generator that generates a predictive crop constituent model that models a relationship between the agricultural characteristic and the crop constituent based on an agricultural characteristic value in the map at the geographic location and the value of the crop constituent detected by the in-situ sensor corresponding to the geographic location; and a predictive map generator that generates a functional predictive crop constituent map of the field, that maps predictive values of the crop constituent to the different locations in the field, based on the agricultural characteristic values in the map and based on the predictive crop constituent model. 